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1
Shangguan, Lingxiao, Yunfei Yin, Qingtao Zhang, Qun Liu, Wei Xie, and Zejiao Dong. "Icing Time Prediction Model of Pavement Based on an Improved SVR Model with Response Surface Approach." Applied Sciences 12, no. 16 (2022): 8109. http://dx.doi.org/10.3390/app12168109.
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Pavement icing imposes a great threat to driving safety and impacts the efficiency of the road transportation system in cold regions. This has attracted research predicting pavement icing time to solve the problems brought about by icing. Different models have been proposed in the past decades to predict pavement icing, within which support vector regression (SVR) is a widely used algorithm for calibrating highly nonlinear relationships. This paper presents a hybrid improved SVR algorithm to predict the time of pavement icing with an enhancement operation by response surface method (RSM) and particle swarm optimization (PSO). RSM is used to increase the number of input data collected onsite. Based on that, the optimal SVR model is established by optimizing the kernel function parameters and penalty coefficient with the particle swarm optimization (PSO) algorithm. The hybrid improved SVR is compared with SVR, PSO-SVR, and RSM-PSO for coefficient of determination (R2), mean absolute error, mean absolute percentage error, and root mean square error to check the effectiveness of PSO and RSM in optimizing SVR. The results show that the combination of two methods in the hybrid improved algorithm has a better optimization capability with R2 of 0.9655 and 0.9318 in a train set and test set, respectively, which outperforms PSO-SVR, RSM-SVR, and SVR. In addition, the R2 of the hybrid improved SVR and PSO-SVR both reach the optimal fitness value approximately at the iteration of 20, which suggests that convergence capacity remains relatively constant with the predictive accuracy being improved.
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Xie, Jiang, Taifeng Sun, Jieyu Zhang, and Wu Zhang. "A New Hybrid Method for Parameter Optimization of SVR." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 2 (2018): 271–79. http://dx.doi.org/10.20965/jaciii.2018.p0271.
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The performance of Support Vector Regression (SVR) depends heavily on its parameters, but some optimization methods based on Grid Search (GS) or evolutionary algorithms still have several issues that must be addressed. This paper proposes a new hybrid method (PSO-SS) that combines Particle Swarm Optimization (PSO) and Scatter Search (SS) to optimize the parameters of the SVR. In PSO-SS, to improve the search capability of PSO and reduce the likelihood of the PSO becoming trapped in the local optimum, the initial PSO population is generated by the diversification generation method and the improvement method of SS, and the velocity updating formula of PSO is improved by adding diversity information. On the StatLib and UCI datasets, our experiments show that the PSO-SS method is an effective parameter optimization method compared with other methods. In addition, an SVR model with its parameters optimized by PSO-SS (PSO-SS-SVR) is used to predict the grain size of aluminum alloys. The experimental results show that the PSO-SS-SVR method outperforms Back Propagation Neural Network (BPNN), PSO-SVR and the empirical model.
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Chen, Jialin, and Hanyin Yang. "A CSI 300 Index Prediction Model Based on PSO-SVR-GRNN Hybrid Method." Mobile Information Systems 2022 (August 4, 2022): 1–10. http://dx.doi.org/10.1155/2022/7419920.
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In this article, a PSO-SVR-GRNN nonparametric hybrid model is proposed for the CSI 300 stock index to forecast the problem. Particle Swarm Optimization (PSO) is utilized to optimize the parameters of the SVR model to enhance the prediction ability of the support vector machine's regression model for the original CSI 300 Index time series. The optimized residual sequence prediction results of the General Regression Neural Network (GRNN) are then used to optimize the time series prediction. The outcomes indicate that the PSO- SVR-GRNN model can greatly improve the prediction accuracy of the CSI 300 Index time series compared with individual models such as PSO-SVR, GRNN, GA-SVR, LSTM, PSO-LSTM, and SVR.
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Wang, Xiping, and Yaqi Wang. "A Hybrid Model of EMD and PSO-SVR for Short-Term Load Forecasting in Residential Quarters." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9895639.
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Short-term load forecasting plays a vital role in the daily operational management of power utility. To improve the forecasting accuracy, this paper proposes a hybrid EMD-PSO-SVR forecasting model for short-term load forecasting based on empirical mode decomposition (EMD), support vector regression (SVR), and particle swarm optimization (PSO), also considering the effects of temperature, weekends, and holidays. EMD is used to decompose the residential electric load data into a number of intrinsic mode function (IMF) components and one residue; then SVR is constructed to forecast these IMFs and residual value individually. In order to gain optimization parameters of SVR, PSO is implemented to automatically perform the parameter selection in SVR modeling. Then all of these forecasting values are reconstructed to produce the final forecasting result for residential electric load data. Compared with the results from the EMD-SVR model, traditional SVR model, and PSO-SVR model, the result indicates that the proposed EMD-PSO-SVR model performs more effectively and more stably in forecasting the residential short-term load.
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Wang, Junhua, Junfei Xu, Yan Lu, et al. "Prediction of Deposition Layer Morphology Dimensions Based on PSO-SVR for Laser–arc Hybrid Additive Manufacturing." Coatings 13, no. 6 (2023): 1066. http://dx.doi.org/10.3390/coatings13061066.
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Laser–arc composite additive manufacturing holds significant potential for a wide range of industrial applications, and the control of morphological dimensions in the deposited layer is a critical aspect of this technology. The width and height dimensions within the deposited layer of laser–arc hybrid additive manufacturing serve as essential indicators of its morphological characteristics, directly influencing the shape quality of the deposited layer. Accurate prediction of the shape dimensions becomes crucial in providing effective guidance for size control. To achieve precise prediction of shape dimensions in laser–arc composite additive manufacturing and ensure effective regulation of the deposited layer’s shape quality, this study introduces a novel approach that combines a particle swarm algorithm (PSO) with an optimized support vector regression (SVR) technique. By optimizing the SVR parameters through the PSO algorithm, the SVR model is enhanced and fine-tuned to accurately predict the shape dimensions of the deposited layers. In this study, a series of 25 laser–arc hybrid additive manufacturing experiments were conducted to compare different approaches. Specifically, the SVR model was built using selected radial basis function (rbf) kernel functions. Furthermore, the penalty factors and kernel parameters of the SVR model were optimized using the particle swarm optimization (PSO) algorithm, leading to the development of a PSO-SVR prediction model for the morphological dimensions of the deposited layers. The performance of the PSO-SVR model was compared with that of the SVR, BPNN, and LightGBM models. Model accuracy was evaluated using a test set, revealing average relative errors of 2.39%, 7.719%, 9.46%, and 5.356% for the PSO-SVR, SVR, BPNN, and LightGBM models, respectively. The PSO-SVR model exhibited excellent prediction accuracy with minimal fluctuations in prediction error. This performance demonstrates the model’s ability to effectively capture the intricate and non-linear relationship between process parameters and deposition layer dimensions. Consequently, the PSO-SVR model can provide a foundation for the control of morphological dimensions in the deposition layer, offering an effective guide for deposition layer morphology dimension control in laser–arc composite additive manufacturing.
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Ikram, Rana Muhammad Adnan, Reham R. Mostafa, Zhihuan Chen, et al. "Advanced Hybrid Metaheuristic Machine Learning Models Application for Reference Crop Evapotranspiration Prediction." Agronomy 13, no. 1 (2022): 98. http://dx.doi.org/10.3390/agronomy13010098.
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Hybrid metaheuristic algorithm (MA), an advanced tool in the artificial intelligence field, provides precise reference evapotranspiration (ETo) prediction that is highly important for water resource availability and hydrological studies. However, hybrid MAs are quite scarcely used to predict ETo in the existing literature. To this end, the prediction abilities of two support vector regression (SVR) models coupled with three types of MAs including particle swarm optimization (PSO), grey wolf optimization (GWO), and gravitational search algorithm (GSA) were studied and compared with single SVR and SVR-PSO in predicting monthly ETo using meteorological variables as inputs. Data obtained from Rajshahi, Bogra, and Rangpur stations in the humid region, northwestern Bangladesh, was used for this purpose as a case study. The prediction precision of the proposed models was trained and tested using nine input combinations and assessed using root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). The tested results revealed that the SVR-PSOGWO model outperformed the other applied soft computing models in predicting ETo in all input combinations, followed by the SVR-PSOGSA, SVR-PSO, and SVR. It was found that SVR-PSOGWO decreases the RMSE of SVR, SVR-PSO, and SVR-PSOGSA by 23%, 27%, 14%, 21%, 19%, and 5% in Rangpur and Bogra stations during the testing stage. The RMSE of the SVR, SVR-PSO, and SVR-PSOGSA reduced by 32%, 20%, and 3%, respectively, employing the SVR-PSOGWO for the Rajshahi Station. The proposed hybrid machine learning model has been recommended as a potential tool for monthly ETo prediction in a humid region and similar climatic regions worldwide.
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Hou, Wenjie, Guanghua Yin, Jian Gu, and Ningning Ma. "Estimation of Spring Maize Evapotranspiration in Semi-Arid Regions of Northeast China Using Machine Learning: An Improved SVR Model Based on PSO and RF Algorithms." Water 15, no. 8 (2023): 1503. http://dx.doi.org/10.3390/w15081503.
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Accurate estimation of crop evapotranspiration (ETc) is crucial for effective irrigation and water management. To achieve this, support vector regression (SVR) was applied to estimate the daily ETc of spring maize. Random forest (RF) as a data pre-processing technique was utilized to determine the optimal input variables for the SVR model. Particle swarm optimization (PSO) was employed to optimize the SVR model. This study used data obtained from field experiments conducted between 2017 and 2019, including crop coefficient and daily meteorological data. The performance of the innovative hybrid RF–SVR–PSO model was evaluated against a standalone SVR model, a back-propagation neural network (BPNN) model and a RF model, using different input meteorological variables. The ETc values were calculated using the Penman–Monteith equation, which is recommended by the FAO, and used as a reference for the models’ estimated values. The results showed that the hybrid RF–SVR–PSO model performed better than all three standalone models for ETc estimation of spring maize. The Nash–Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) ranges were 0.956–0.958, 0.275–0.282 mm d−1, 0.221–0.231 mm d−1 and 0.957–0.961, respectively. It is proved that the hybrid RF–SVR–PSO model is appropriate for estimation of daily spring maize ETc in semi-arid regions.
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Hameed, Mohammed Majeed, Mustafa Abbas Abed, Nadhir Al-Ansari, and Mohamed Khalid Alomar. "Predicting Compressive Strength of Concrete Containing Industrial Waste Materials: Novel and Hybrid Machine Learning Model." Advances in Civil Engineering 2022 (March 23, 2022): 1–19. http://dx.doi.org/10.1155/2022/5586737.
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In the construction and cement manufacturing sectors, the development of artificial intelligence models has received remarkable progress and attention. This paper investigates the capacity of hybrid models conducted for predicting the compressive strength (CS) of concrete where the cement was partially replaced with ground granulated blast-furnace slag ( FS ) and fly ash ( FA ) materials. Accurate estimation of CS can reduce the cost and laboratory tests. Since the traditional method of calculation CS is complicated and requires lots of effort, this article presents new predictive models called SVR − PSO and SVR − GA , that are a hybridization of support vector regression ( SVR ) with improved particle swarm algorithm ( PSO ) and genetic algorithm ( GA ). Furthermore, the hybrid models (i.e., SVR − PSO and SVR − GA ) were used for the first time to predict CS of concrete where the cement component is partially replaced. The improved PSO and GA are given essential roles in tuning the hyperparameters of the SVR model, which have a significant influence on model accuracy. The suggested models are evaluated against extreme learning machine (ELM) via quantitative and visual evaluations. The models are evaluated using eight statistical parameters, and then the SVR-PSO has provided the highest accuracy than comparative models. For instance, the SVR − PSO during the testing phase provided fewer root mean square error RMSE with 1.386 MPa, a higher Nash–Sutcliffe model efficiency coefficient ( NE ) of 0.972, and lower uncertainty at 95% ( U 95 ) with 28.776%. On the other hand, the SVR − GA and ELM models provide lower accuracy with RMSE of 2.826 MPa and 2.180, NE with 0.883 and 0.930, and U 95 with 518.686 183.182, respectively. Sensitivity analysis is carried out to select the influential parameters that significantly affect CS . Overall, the proposed model showed a good prediction of CS of concrete where cement is partially replaced and outperformed 14 models developed in the previous studies.
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Mohanad, S. AL-Musaylh, C. Deo Ravinesh, and Li Yan. "Particle Swarm Optimized–Support Vector Regression Hybrid Model for Daily Horizon Electricity Demand Forecasting Using Climate Dataset." E3S Web of Conferences 64 (2018): 08001. http://dx.doi.org/10.1051/e3sconf/20186408001.
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This paper has adopted six daily climate variables for the eleven major locations, and heavily populated areas in Queensland, Australia obtained from Scientific Information for Land Owners (SILO) to forecast the daily electricity demand (G) obtained from the Australian Energy Market Operator (AEMO). Optimal data-driven technique based on a support vector regression (SVR) model was applied in this study for the G forecasting, where the model’s parameters were selected using a particle swarm optimization (PSO) algorithm. The performance of PSO–SVR was compared with multivariate adaptive regression spline (MARS) and the traditional model of SVR. The results showed that the PSO–SVR model outperformed MARS and SVR.
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Nguyen, Hoang, Yosoon Choi, Xuan-Nam Bui, and Trung Nguyen-Thoi. "Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms." Sensors 20, no. 1 (2019): 132. http://dx.doi.org/10.3390/s20010132.
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In this study, vibration sensors were used to measure blast-induced ground vibration (PPV). Different evolutionary algorithms were assessed for predicting PPV, including the particle swarm optimization (PSO) algorithm, genetic algorithm (GA), imperialist competitive algorithm (ICA), and artificial bee colony (ABC). These evolutionary algorithms were used to optimize the support vector regression (SVR) model. They were abbreviated as the PSO-SVR, GA-SVR, ICA-SVR, and ABC-SVR models. For each evolutionary algorithm, three forms of kernel function, linear (L), radial basis function (RBF), and polynomial (P), were investigated and developed. In total, 12 new hybrid models were developed for predicting PPV in this study, named ABC-SVR-P, ABC-SVR-L, ABC-SVR-RBF, PSO-SVR-P, PSO-SVR-L, PSO-SVR-RBF, ICA-SVR-P, ICA-SVR-L, ICA-SVR-RBF, GA-SVR-P, GA-SVR-L and GA-SVR-RBF. There were 125 blasting results gathered and analyzed at a limestone quarry in Vietnam. Statistical criteria like R2, RMSE, and MAE were used to compare and evaluate the developed models. Ranking and color intensity methods were also applied to enable a more complete evaluation. The results revealed that GA was the most dominant evolutionary algorithm for the current problem when combined with the SVR model. The RBF was confirmed as the best kernel function for the GA-SVR model. The GA-SVR-RBF model was proposed as the best technique for PPV estimation.
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Wang, Yuxin, Yuan Yuan, Ye Pan, and Zhengqiu Fan. "Modeling Daily and Monthly Water Quality Indicators in a Canal Using a Hybrid Wavelet-Based Support Vector Regression Structure." Water 12, no. 5 (2020): 1476. http://dx.doi.org/10.3390/w12051476.
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Accurate prediction of water quality indicators plays an important role in the effective management of water resources. The models which studied limited water quality indicators in natural rivers may give inadequate guidance for managing a canal being used for water diversion. In this study, a hybrid structure (WA-PSO-SVR) based on wavelet analysis (WA) coupled with support vector regression (SVR) and particle swarm optimization (PSO) algorithms was developed to model three water quality indicators, chemical oxygen demand determined by KMnO4 (CODMn), ammonia nitrogen (NH3-N), and dissolved oxygen (DO), in water from the Grand Canal from Beijing to Hangzhou. Modeling was independently conducted over daily and monthly time scales. The results demonstrated that the hybrid WA-PSO-SVR model was able to effectively predict non-linear stationary and non-stationary time series and outperformed two other models (PSO-SVR and a standalone SVR), especially for extreme values prediction. Daily predictions were more accurate than monthly predictions, indicating that the hybrid model was more suitable for short-term predictions in this case. It also demonstrated that using the autocorrelation and partial autocorrelation of time series enabled the construction of appropriate models for water quality prediction. The results contribute to water quality monitoring and better management for water diversion.
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Bui, Xuan-Nam, Chang Woo Lee, Hoang Nguyen, et al. "Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO." Applied Sciences 9, no. 14 (2019): 2806. http://dx.doi.org/10.3390/app9142806.
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Dust is one of the components causing heavy environmental pollution in open-pit mines, especially PM10. Some pathologies related to the lung, respiratory system, and occupational diseases have been identified due to the effects of PM10 in open-pit mines. Therefore, the prediction and control of PM10 concentration in the production process are necessary for environmental and health protection. In this study, PM10 concentration from drilling operations in the Coc Sau open-pit coal mine (Vietnam) was investigated and considered through a database including 245 datasets collected. A novel hybrid artificial intelligence model was developed based on support vector regression (SVR) and a swarm optimization algorithm (i.e., particle swarm optimization (PSO)), namely PSO-SVR, for estimating PM10 concentration from drilling operations at the mine. Polynomial (P), radial basis function (RBF), and linear (L) kernel functions were considered and applied to the development of the PSO-SVR models in the present study, abbreviated as PSO-SVR-P, PSO-SVR-RBF, and PSO-SVR-L. Also, three benchmark artificial intelligence techniques, such as k-nearest neighbors (KNN), random forest (RF), and classification and regression trees (CART), were applied and developed for estimating PM10 concentration and then compared with the PSO-SVR models. Root-mean-squared error (RMSE) and determination coefficient (R2) were used as the statistical criteria for evaluating the performance of the developed models. The results exhibited that the PSO algorithm had an essential role in the optimization of the hyper-parameters of the SVR models. The PSO-SVR models (i.e., PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF) had higher performance levels than the other models (i.e., RF, CART, and KNN) with an RMSE of 0.040, 0.042, and 0.043; and R2 of 0.954, 0.948, and 0.946; for the PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF models, respectively. Of these PSO-SVR models, the PSO-SVR-L model was the most dominant model with an RMSE of 0.040 and R2 of 0.954. The remaining three benchmark models (i.e., RF, CART, and KNN) yielded a more unsatisfactory performance with an RMSE of 0.060, 0.052, and 0.067; and R2 of 0.894, 0.924, and 0.867, for the RF, CART, and KNN models, respectively. Furthermore, the findings of this study demonstrated that the density of rock mass, moisture content, and the penetration rate of the drill were essential parameters on the PM10 concentration caused by drilling operations in open-pit mines.
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Wang, Meiping, and Qi Tian. "Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/3968324.
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We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR) model-optimized particle swarm optimization (PSO) algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR), and artificial neural network (ANN) through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.
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Hong, Wei-Chiang, and Guo-Feng Fan. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting." Energies 12, no. 6 (2019): 1093. http://dx.doi.org/10.3390/en12061093.
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For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization (PSO) algorithm have been successfully hybridized with the support vector regression (SVR) to produce satisfactory forecasting performance in previous studies. Decomposed intrinsic mode functions (IMFs), could be further defined as three items: item A contains the random term and the middle term; item B contains the middle term and the trend (residual) term, and item C contains the middle terms only, where the random term represents the high-frequency part of the electric load data, the middle term represents the multiple-frequency part, and the trend term represents the low-frequency part. These three items would be modeled separately by the SVR-PSO model, and the final forecasting results could be calculated as A+B-C (the defined item D). Consequently, this paper proposes a novel electric load forecasting model, namely H-EMD-SVR-PSO model, by hybridizing these three defined items to improve the forecasting accuracy. Based on electric load data from the Australian electricity market, the experimental results demonstrate that the proposed H-EMD-SVR-PSO model receives more satisfied forecasting performance than other compared models.
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Xu, Huan. "Prediction of Students’ Performance Based on the Hybrid IDA-SVR Model." Complexity 2022 (February 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/1845571.
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Students’ performance is an important factor for the evaluation of teaching quality in colleges. The aim of this study is to propose a novel intelligent approach to predict students’ performance using support vector regression (SVR) optimized by an improved duel algorithm (IDA). To the best of our knowledge, few research studies have been developed to predict students’ performance based on student behavior, and the novelty of this study is to develop a new hybrid intelligent approach in this field. According to the obtained results, the IDA-SVR model clearly outperformed the other models by achieving less mean square error (MSE). In other words, IDA-SVR with an MSE of 0.0089 has higher performance than DT with an MSE of 0.0326, SVR with an MSE of 0.0251, ANN with an MSE of 0.0241, and PSO-SVR with an MSE of 0.0117. To investigate the efficacy of IDA, other parameter optimization methods, that is, the direct determination method, grid search method, GA, FA, and PSO, are used for a comparative study. The results show that the IDA algorithm can effectively avoid the local optima and the blindness search and can definitely improve the speed of convergence to the optimal solution.
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Soltani, Jaber, Moosa Kalanaki, and Mohammad Soltani. "Fuzzified Pipes Dataset to Predict Failure Rates by Hybrid SVR-PSO Algorithm." Modern Applied Science 10, no. 7 (2016): 29. http://dx.doi.org/10.5539/mas.v10n7p29.
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This paper proposes a Support Vector Regression (SVR) based on Fuzzified Input-output Variables which has good comprehensibility as well as satisfactory generalization capability. SVM provides a mechanism to predict data from training ones. Then, results from proposed Fuzzified SVR-PSO (FSVR-PSO) model are compared with other methods; comparative tests are performed using pipe failures data. The analysis and the experimental results show this method has high comprehensibility as well as satisfactory generalization capability.
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Wang, Jianzhou, Qingping Zhou, Haiyan Jiang, and Ru Hou. "Short-Term Wind Speed Forecasting Using Support Vector Regression Optimized by Cuckoo Optimization Algorithm." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/619178.
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This paper develops an effectively intelligent model to forecast short-term wind speed series. A hybrid forecasting technique is proposed based on recurrence plot (RP) and optimized support vector regression (SVR). Wind caused by the interaction of meteorological systems makes itself extremely unsteady and difficult to forecast. To understand the wind system, the wind speed series is analyzed using RP. Then, the SVR model is employed to forecast wind speed, in which the input variables are selected by RP, and two crucial parameters, including the penalties factor and gamma of the kernel function RBF, are optimized by various optimization algorithms. Those optimized algorithms are genetic algorithm (GA), particle swarm optimization algorithm (PSO), and cuckoo optimization algorithm (COA). Finally, the optimized SVR models, including COA-SVR, PSO-SVR, and GA-SVR, are evaluated based on some criteria and a hypothesis test. The experimental results show that (1) analysis of RP reveals that wind speed has short-term predictability on a short-term time scale, (2) the performance of the COA-SVR model is superior to that of the PSO-SVR and GA-SVR methods, especially for the jumping samplings, and (3) the COA-SVR method is statistically robust in multi-step-ahead prediction and can be applied to practical wind farm applications.
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Kaloop, Mosbeh R., Bishwajit Roy, Kuldeep Chaurasia, et al. "Shear Strength Estimation of Reinforced Concrete Deep Beams Using a Novel Hybrid Metaheuristic Optimized SVR Models." Sustainability 14, no. 9 (2022): 5238. http://dx.doi.org/10.3390/su14095238.
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This study looks to propose a hybrid soft computing approach that can be used to accurately estimate the shear strength of reinforced concrete (RC) deep beams. Support vector regression (SVR) is integrated with three novel metaheuristic optimization algorithms: African Vultures optimization algorithm (AVOA), particle swarm optimization (PSO), and Harris Hawks optimization (HHO). The proposed models, SVR-AVOA, -PSO, and -HHO, are designed and compared to reference existing models. Multi variables are used and evaluated to model and evaluate the deep beam’s shear strength, and the sensitivity of the selected variables in modeling the shear strength is assessed. The results indicate that the SVR-AVOA outperforms other proposed and existing models for the shear strength prediction. The mean absolute error of SVR-AVOA, SVR-PSO, and SVR-HHO are 43.17 kN, 44.09 kN, and 106.95 kN, respectively. The SVR-AVOA can be used as a soft computing technique to estimate the shear strength of the RC deep beam with a maximum error of ±3.39%. Furthermore, the sensitivity analysis shows that the deep beam’s key parameters (shear span to depth ratio, web reinforcement’s yield strength, concrete compressive strength, stirrups spacing, and the main longitudinal bars reinforcement ratio) are efficiently impacted in the shear strength detection of RC deep beam.
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Wang, Liang, Yang Xia, and Yichun Lu. "A Novel Forecasting Approach by the GA-SVR-GRNN Hybrid Deep Learning Algorithm for Oil Future Prices." Computational Intelligence and Neuroscience 2022 (August 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/4952215.
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It is hard to forecasting oil future prices accurately, which is affected by some nonlinear, nonstationary, and other chaotic characteristics. Then, a novel GA-SVR-GRNN hybrid deep learning algorithm is put forward for forecasting oil future price. First, a genetic algorithm (GA) is employed for optimizing parameters regarding the support vector regression machine (SVR), and the GA-SVR model is used to forecast oil future price. Further, a generalized regression neural network (GRNN) model is built for the residual series for forecasting. Finally, we obtain the predicted values of the oil future price series forecasted by the GA-SVR-GRNN hybrid deep learning algorithm. According to the simulation, the GA-SVR-GRNN hybrid deep learning algorithm achieves lower MSE, RMSE, MAE, and MAPE relative to the GRNN, GA-SVR, and PSO-SVR models, indicating that the proposed GA-SVR-GRNN hybrid deep learning algorithm can fully reveal the prediction advantages of the GA-SVR and GRNN models in the nonlinear space and is a more accurate and effective method for oil future price forecasting.
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Zhang, Wei, Liyi Li, Baoping Zhang, Xin Xu, Jian Zhai, and Junwen Wang. "A Closed-Loop Optimized System with CFD Data for Liquid Maldistribution Model." Processes 8, no. 11 (2020): 1332. http://dx.doi.org/10.3390/pr8111332.
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For the simulation of a trickle-bed reactor (TBR) in coal and oil refining, modeling the liquid maldistribution of the gas-liquid distributor incurs enormous pre-processing work and bears a huge computational cost. A closed-loop optimized system with computational fluid dynamic (CFD) data is therefore proposed for the first time in this paper. A fast prediction model based on support vector regression (SVR) is developed to simplify the modeling of the liquid flow rate in TBRs. The model uses CFD simulation results to determine an optimized set of structural parameters for the gas-liquid distributor in TBRs. In order to obtain an accurate SVR model quickly, the particle swarm optimization (PSO) algorithm is employed to optimize the SVR parameters. Then, the structural parameters corresponding to the minimum liquid maldistribution factor are calculated using the response surface methodology (RSM) based on the hybrid PSO-SVR model. The CFD validation results show a good agreement with the values predicted by RSM, with liquid maldistribution factors of 0.159 and 0.162, respectively.
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Jaleel, Abdul, and K. Aparna. "Identification of heat-integrated distillation column using hybrid support vector regression and particle swarm optimization." Chemical Industry and Chemical Engineering Quarterly 24, no. 2 (2018): 101–15. http://dx.doi.org/10.2298/ciceq161118023j.
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Distillation is the most commonly used method for separating fluid mixtures in oil and gas industries. It is a process that requires high energy usage. One of the efficient ways to save energy in a distillation column is by heat integration. One such type of distillation column is called a heat-integrated distillation column (HIDC). In HIDC, the prediction of mole fractions of the component in the product can be made using proper identification, or modeling, of the HIDC. However, nonlinear modeling of HIDC is a highly challenging task. Methods based on first principles are not sufficient for a highly nonlinear HIDC. Hence, a novel method for identification of HIDC using a non-parametric ?support vector regression (SVR)? method for predicting benzene composition in benzene-toluene HIDC is proposed in this work. The data used for identification is generated using process simulation software HYSYS. 100 samples of data were used for training and 50 samples of data were employed for validating the model. Particle swarm optimization (PSO) was also incorporated with SVR for obtaining optimized parameters of SVR. The proposed model is compared with other SVR models optimized with optimization methods other than PSO. The proposed model showed better performance over others.
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Li, Weide, and Juan Zhang. "An innovated integrated model using Singular Spectrum Analysis and Support Vector Regression optimized by intelligent algorithm for rainfall forecasting." Journal of Autonomous Intelligence 2, no. 1 (2019): 46. http://dx.doi.org/10.32629/jai.v2i1.37.
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Rainfall forecasting is becoming more and more significant and precipitation anomalies would lead to droughts and floods disasters. However, because of the complexity and non-stationary of rainfall data, it is difficult to forecast. In this paper, a novel hybrid model to forecast rainfall is developed by incorporating singular spectrum analysis (SSA) and dragonfly algorithm (DA) into support vector regression (SVR) method. Firstly, SSA is used for extracting the trend components of the hydrological data. Then, SVR is utilized to deal with the volatility and irregularity of the precipitation series. Finally, the parameter of SVR is optimized by DA. The proposed SSA-DA-SVR method is used to forecast the monthly precipitation for Songbai, Panshui, Lanma and Jiulongchi stations. To validate the efficiency of the method, four compared models, DA-SVR, SSA-GWO-SVR, SSA-PSO-SVR, SSA-CS-SVR are established. The result shows the proposed method has the best performance among all five models, and its prediction has high precision and accuracy.
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Banihabib, Mohammad Ebrahim, Lubos Jurik, Mahsa Sheikh Kazemi, Jaber Soltani, and Mitra Tanhapour. "A Hybrid Intelligence Model for the Prediction of the Peak Flow of Debris Floods." Water 12, no. 8 (2020): 2246. http://dx.doi.org/10.3390/w12082246.
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Debris floods, as one of the most significant natural hazards, often threaten the lives and property of many people worldwide. Predicting models are essential for flood warning systems to minimize casualties of debris floods. Since HEC-HMS (Hydrologic Engineering Center’s Hydrological Modelling System) cannot simulate debris flow, this study proposes a new hybrid model that uses artificial intelligence models to overcome HEC-HMS’s insufficiency in reflecting the sediment concentration effect on the debris floods. A sediment concentration is an effective factor for evaluating debris flood peak flows. This led to the proposal of new hybrid models for predicting the debris flood peak flows on the basis of hybridization of the artificial intelligence models (Bayesian Network (BN) and Support Vector Regression–Particle Swarm Optimization (SVR-PSO)) and HEC-HMS. To estimate the sediment concentration of floods by using the proposed artificial intelligence models, we nominated an average basin elevation, an average basin slope, a basin area, the current day rainfall, the antecedent rainfall of the past 3 days, and the streamflow of the previous day the previous day as the effective variables. In the validation stage, the average of the Mean Absolute Relative Error (MARE) of the estimated values were 0.024, 0.038, and 0.024 for the typical floods that occurred in the Navrood, Kasilian, and the Amameh basins in the north of Iran, respectively. Similarly, we obtained values of 0.038, 0.073, and 0.040 for the debris flood events for the three respective locations. After predicting the debris flood peak flows by the proposed hybrid HMS-BN and HMS-SVR-PSO models, the average of the MAREs for all debris flood events was reduced to 0.013 and 0.014, respectively. The comparison of MAREs of the examined hybrid models shows that the HMS-BN model results in higher accuracy than the HMS-SVR-PSO model in the prediction of the debris flood peak flows. Generally, the absolute error of prediction by the proposed hybrid model is reduced to one-third of the HEC-HMS. The prediction of the debris flood peak flows using the proposed hybrid model can be examined in the debris flood warning systems to reduce the potential damages and casualties in similar basins.
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PEI, J. F., C. Z. CAI, and Y. M. ZHU. "MODELING AND PREDICTING THE GLASS TRANSITION TEMPERATURE OF VINYL POLYMERS BY USING HYBRID PSO-SVR METHOD." Journal of Theoretical and Computational Chemistry 12, no. 03 (2013): 1350002. http://dx.doi.org/10.1142/s0219633613500028.
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Based on four physicochemical descriptors (the rigidness descriptor R OM resulted by hydrogen-bonding moieties group and/or rings, the chain mobility n, the molecular average polarizability α and the net charge of the most negative atom q-) derived from the polymers' monomers structure, the support vector regression (SVR) approach combined with particle swarm optimization (PSO), is proposed to construct a model for prediction of the glass transition temperature T g of three classes of vinyl polymers, including polystyrenes, polyacrylates and polymethacrylates. The mean absolute error (MAE = 13.68 K), mean absolute percentage error (MAPE = 4.22%) and correlation coefficient (R2 = 0.9252) calculated by SVR are superior to those (MAE = 16.74 K, MAPE = 5.30% and R2 = 0.9059) achieved by S-SAR model, and (MAE = 16.83 K, MAPE = 5.27% and R2 = 0.9057) achieved by ANN model for the identical training set (124 vinyl polymers), whereas the MAE = 15.09 K, MAPE = 4.82% and R2 = 0.9253 calculated by SVR are also better than those of MAE = 17.96 K, MAPE = 5.94% and R2 = 0.8952 achieved by S-SAR, and MAE = 16.603 K, MAPE = 5.4% and R2 = 0.9120 achieved by ANN for the same 68 test samples. Furthermore, the MAE, MAPE and R2 for an independent set (10 vinyl polymers) predicted by SVR also reached 14.132 K, 4.25% and 0.9475, respectively. The results strongly support that the comprehensive modeling and prediction ability of SVR model surpass those of S-SAR and ANN models by applying identical training, test and independent samples. It is demonstrated that the established SVR model is more suitable to be used for prediction of the T g values for unknown vinyl polymers possessing similar structure than S-SAR model or ANN model.
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Yang, Ziquan, Yanqi Wu, Yisong Zhou, Hui Tang, and Shanchun Fu. "Assessment of Machine Learning Models for the Prediction of Rate-Dependent Compressive Strength of Rocks." Minerals 12, no. 6 (2022): 731. http://dx.doi.org/10.3390/min12060731.
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The prediction of rate-dependent compressive strength of rocks in dynamic compression experiments is still a notable challenge. Four machine learning models were introduced and employed on a dataset of 164 experiments to achieve an accurate prediction of the rate-dependent compressive strength of rocks. Then, the relative importance of the seven input features was analyzed. The results showed that compared with the extreme learning machine (ELM), random forest (RF), and the original support vector regression (SVR) models, the correlation coefficient R2 of prediction results with the hybrid model that combines the particle swarm optimization (PSO) algorithm and SVR was highest in both the training set and the test set, both exceeding 0.98. The PSO-SVR model obtained a higher prediction accuracy and a smaller prediction error than the other three models in terms of evaluation metrics, which showed the possibility of the model as a rate-dependent compressive strength prediction tool. Additionally, besides the static compressive strength, the stress rate is the most important influence factor on the rate-dependent compressive strength of the rock among the listed input parameters. Moreover, the strain rate has a positive effect on the rock strength.
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Nur, Arip Syaripudin, Yong Je Kim, Junho Lee, and Chang-Wook Lee. "Spatial Prediction of Wildfire Susceptibility Using Hybrid Machine Learning Models Based on Support Vector Regression in Sydney, Australia." Remote Sensing 15, no. 3 (2023): 760. http://dx.doi.org/10.3390/rs15030760.
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Australia has suffered devastating wildfires recently, and is predisposed to them due to several factors, including topography, meteorology, vegetation, and ignition sources. This study utilized a geographic information system (GIS) technique to analyze and understand the factors that regulate the spatial distribution of wildfire incidents and machine learning to predict wildfire susceptibility in Sydney. Wildfire inventory data were constructed by combining the fire perimeter through field surveys and fire occurrence data gathered from the visible infrared imaging radiometer suite (VIIRS)-Suomi thermal anomalies product between 2011 and 2020 for the Sydney area. Sixteen wildfire-related factors were acquired to assess the potential of machine learning based on support vector regression (SVR) and various metaheuristic approaches (GWO and PSO) for wildfire susceptibility mapping in Sydney. In addition, the 2019–2020 “Black Summer” fire acted as a validation dataset to assess the predictive capability of the developed model. Furthermore, the information gain ratio (IGR) method showed that driving factors such as land use, forest type, and slope degree have a large impact on wildfire susceptibility in the study area, and the frequency ratio (FR) method represented how the factors influence wildfire occurrence. Model evaluation based on area under the curve (AUC) and root average square error (RMSE) were used, and the outputs showed that the hybrid-based SVR-PSO (AUC = 0.882, RMSE = 0.006) model performed better than the standalone SVR (AUC = 0.837, RMSE = 0.097) and SVR-GWO (AUC = 0.873, RMSE = 0.080) models. Thus, optimizing SVR with metaheuristics improved the accuracy of wildfire susceptibility modeling in the study area. The proposed framework can be an alternative to the modeling approach and can be adapted for any research related to the susceptibility of different disturbances.
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TANG, J. L., C. Z. CAI, T. T. XIAO, and S. J. HUANG. "MODELING AND PREDICTING THE ELECTRICAL CONDUCTIVITY OF COMPOSITE CATHODE FOR SOLID OXIDE FUEL CELL BY USING SUPPORT VECTOR REGRESSION." International Journal of Modern Physics B 26, no. 13 (2012): 1250093. http://dx.doi.org/10.1142/s0217979212500932.
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The electrical conductivity of solid oxide fuel cell (SOFC) cathode is one of the most important indices affecting the efficiency of SOFC. In order to improve the performance of fuel cell system, it is advantageous to have accurate model with which one can predict the electrical conductivity. In this paper, a model utilizing support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter optimization was established to modeling and predicting the electrical conductivity of Ba 0.5 Sr 0.5 Co 0.8 Fe 0.2 O 3-δ-x Sm 0.5 Sr 0.5 CoO 3-δ (BSCF–xSSC) composite cathode under two influence factors, including operating temperature (T) and SSC content (x) in BSCF–xSSC composite cathode. The leave-one-out cross validation (LOOCV) test result by SVR strongly supports that the generalization ability of SVR model is high enough. The absolute percentage error (APE) of 27 samples does not exceed 0.05%. The mean absolute percentage error (MAPE) of all 30 samples is only 0.09% and the correlation coefficient (R2) as high as 0.999. This investigation suggests that the hybrid PSO–SVR approach may be not only a promising and practical methodology to simulate the properties of fuel cell system, but also a powerful tool to be used for optimal designing or controlling the operating process of a SOFC system.
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Abood, Emad A., Mustafa Kamal Al-Kamal, Sabih Hashim Muhodir, et al. "Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization." Buildings 15, no. 2 (2025): 191. https://doi.org/10.3390/buildings15020191.
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In structures with reinforced concrete walls, coupling beams join individual walls to produce a rigid assembly that withstands sideways forces. A precise forecasting of the critical shear capacity is essential to avoid early shear failure and attain the desired ductility performance of coupled shear wall systems in earthquake design. This paper examines the ability of Support Vector Regression (SVR) in predicting the shear performance of coupling beams. SVR is a distinguished machine learning regression method that has been positively utilized in former works to forecast the performance of several structural members. Nevertheless, the capability of this regression method deeply relies on picking its best hyperparameters. To handle this, a heuristic optimization procedure named Particle Swarm Optimization (PSO) was merged with SVR to select the optimal hyperparameters. The data of RC coupling beams collected from the previous works were utilized to build the proposed model. Several performance metrics, including RMSE, R2, and MAE, were employed to compare the performance of the optimized model against a baseline SVR model and previous approaches. Analytical results indicate that the new optimized prediction model can assist civil engineers in designing RC coupling beam structures more effectively and outperforms existing models in predicting the shear strength of such beams.
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Deng, Guoqu, Hu Chen, and Siqi Wang. "Risk Assessment and Prediction of Rainstorm and Flood Disaster Based on Henan Province, China." Mathematical Problems in Engineering 2022 (February 18, 2022): 1–17. http://dx.doi.org/10.1155/2022/5310920.
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To reasonably evaluate and predict the loss of rainstorm and flood disaster, this study is based on the rainfall data and rainstorm and flood disaster data of 18 cities in Henan Province from 2010 to 2020, using GIS technology and weighted comprehensive evaluation method to analyze the risk of rainstorm and flood disaster factors in various regions. The four risk factors of hazard risk, hazard-pregnant environment sensitivity, hazard-bearing body vulnerability, and disaster resilience were analyzed in compartment analysis. At the same time, a new rainstorm and flood disaster prediction model was constructed in combination with the hybrid PSO-SVR algorithm. The research results show that there are many rivers in Henan Province, the terrain tends to be higher in the west and lower in the east, and most areas are low plains, making most cities in Henan Province at a moderate risk level. For the more developed cities such as Zhengzhou, Luoyang, and Nanyang, the hazard risk, sensitivity, vulnerability, and disaster resistance are high, and they are prone to heavy rains and floods. For the economically underdeveloped, the terrain is high or hills, such as Sanmenxia City; Xinyang City and other places have low hazard risk and are not prone to rainstorms and floods. By constructing a hybrid PSO-SVR model, selecting two representative cities of Zhengzhou and Luoyang, and predicting the daily rainfall, the number of disasters, and the direct economic loss, the calculated RMSE and MAPE values are both less than GA-SVR, the traditional SVR, and BPNN models, which have verified the superiority of the model proposed in this study and the practical value it brings. To further verify the prediction accuracy of the hybrid model, the average value of RMSE and MAPE of other 16 cities are calculated, and the result is still smaller than other three models, and the study can provide some decision-making references for the urban rainstorm and flood management.
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Taghipour Gorji, Reza, Seyyed Mehdi Hosseini, Ali Akbar Abdoos, and Ali Ebadi. "A Hybrid Intelligent Method for Compensation of Current Transformers Saturation Based on PSO-SVR." Periodica Polytechnica Electrical Engineering and Computer Science 65, no. 1 (2021): 53–61. http://dx.doi.org/10.3311/ppee.16248.
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The Current Transformers (CT) saturation may cause the protective relays mal-operation either non-recognition of internal fault or undesirable trip under external fault conditions. Therefore, compensation of CT saturation is very important for correct performance of protective schemes. Compensation of CT saturation by combination of signal processing methods and intelligent algorithms is a suitable solution to solve the problem. It decreases the probability of mal-operation and increases the reliability of the power system. In this paper, Support Vector Regression (SVR) method is employed to compensate the distorted secondary current due to CT saturation. In SVR method, despite the other methods such as MLPand ANFIS, instead of minimizing the model error, the operational risk error is considered as target function. In this method, by using Kernel tricks, a smart RBF neural network is obtained, so that all operational procedures will be optimized automatically. In this paper, an intelligent method based on Particle Swarm Optimization (PSO) algorithm is presented to determine the optimal values of SVR parameters. Due to the stability and robustness of this method in presence of noise and sudden changes in current, this method has a high accuracy. In addition, a sample power system is simulated using PSCAD software. Afterwards, current signals are extracted and fed to PSO-SVR algorithm, which is implemented in MATLAB environment. The obtained results show the preference of the proposed method in aspect of estimation accuracy as compared to some presented methods in the field of CT saturation detection and correction.
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Chen, Xuejun, Shiqiang Jin, Shanshan Qin, and Laping Li. "Short-Term Wind Speed Forecasting Study and Its Application Using a Hybrid Model Optimized by Cuckoo Search." Mathematical Problems in Engineering 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/608597.
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The support vector regression (SVR) and neural network (NN) are both new tools from the artificial intelligence field, which have been successfully exploited to solve various problems especially for time series forecasting. However, traditional SVR and NN cannot accurately describe intricate time series with the characteristics of high volatility, nonstationarity, and nonlinearity, such as wind speed and electricity price time series. This study proposes an ensemble approach on the basis of 5-3 Hanning filter (5-3H) and wavelet denoising (WD) techniques, in conjunction with artificial intelligence optimization based SVR and NN model. So as to confirm the validity of the proposed model, two applicative case studies are conducted in terms of wind speed series from Gansu Province in China and electricity price from New South Wales in Australia. The computational results reveal that cuckoo search (CS) outperforms both PSO and GA with respect to convergence and global searching capacity, and the proposed CS-based hybrid model is effective and feasible in generating more reliable and skillful forecasts.
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Alves, Emilly Pereira, João Fausto Lorenzato Oliveira, Francisco Madeiro Bernardino Junior, and Manoel Henrique da Nóbrega Marinho. "A Nonlinear Optimized Hybrid System For Energy Consumption Forecasting From Smart Meters." Learning and Nonlinear Models 20, no. 1 (2022): 17–30. http://dx.doi.org/10.21528/lnlm-vol20-no1-art2.
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Smart grids are an alternative to minimize environmental impacts, such as CO2 emissions, and improve the efficiency of electricity consumption in buildings. Power grids enable adequate management and monitoring of consumption because of the periodic storage of measurements and easy access to them. In this scenario, an accurate prediction is a challenging task. Forecasting of consumption series is a defiant problem because data present linear and nonlinear patterns, and a dependence on external variables may be observed. Hybrid models are an alternative to mapping both patterns, which have been widely used to forecast load time series. Autoregressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR) models are used for this purpose, to map the linear and nonlinear patterns of the series, respectively. In this paper, a nonlinear optimized hybrid system based on ARIMA, SVR, and Particle Swarm Optimization (PSO) is proposed. The system can be divided into three steps. First, the linear patterns are predicted by the statistical model ARIMA. Then, the residual series is modeled using an optimized SVR, in which the parameters are selected from the PSO. One particularity from the proposal is to incorporate the choice of the topology and the inertia coefficient into the system. Lastly, the predictions are combined using the SVR. The simulations were conducted using a real database from smart meters of a building in Taiwan. To evaluate the performance of the proposed method, four related approaches were implemented and compared: a single ARIMA, two linear combination systems, and one non-linear combination system. The results show a superiority of the proposed method in terms of the metrics Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).
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Kazemi, Mahsa Sheikh, Mohammad Ebrarim Banihabib, and Jaber Soltani. "A hybrid SVR-PSO model to predict concentration of sediment in typical and debris floods." Earth Science Informatics 14, no. 1 (2021): 365–76. http://dx.doi.org/10.1007/s12145-021-00570-0.
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Fan, Guo-Feng, Li-Ling Peng, Xiangjun Zhao, and Wei-Chiang Hong. "Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model." Energies 10, no. 11 (2017): 1713. http://dx.doi.org/10.3390/en10111713.
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Hasanipanah, Mahdi, Azam Shahnazar, Hassan Bakhshandeh Amnieh, and Danial Jahed Armaghani. "Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model." Engineering with Computers 33, no. 1 (2016): 23–31. http://dx.doi.org/10.1007/s00366-016-0453-2.
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Hou, Chunyao, Yilun Wei, Hongyi Zhang, et al. "Stress Prediction Model of Super-High Arch Dams Based on EMD-PSO-GPR Model." Water 15, no. 23 (2023): 4087. http://dx.doi.org/10.3390/w15234087.
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In response to the challenge of limited model availability for predicting the lifespan of super-high arch dams, a hybrid model named EMD-PSO-GPR (EPR) is proposed in this study. The EPR model leverages Empirical Mode Decomposition (EMD), Gaussian Process Regression (GPR), and Particle Swarm Optimization (PSO) to provide an effective solution for super-high arch dam stress prediction. This research focuses on three strategically selected measurement points within the dam, characterized by complex stress conditions. The predicted results from the EPR are compared with those from GPR, Long Short-Term Memory (LSTM), and Support Vector Regression (SVR), using actual stress data measured at research points within a super-high arch dam in Southwest China. The findings reveal that the proposed EPR model attains a maximum mean absolute error (MAE) of 0.02916 and a maximum root mean square error (RMSE) of 0.03055, surpassing the compared models. As a result, the EPR model introduces an innovative computational framework for stress prediction in super-high arch dams, excelling in handling stress data characterized by high vibration frequencies and providing more accurate predictions.
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Fidalgo Astorquia, Ignacio, Nerea Gómez-Larrakoetxea, Juan J. Gude, and Iker Pastor. "Fractional-Order System Identification: Efficient Reduced-Order Modeling with Particle Swarm Optimization and AI-Based Algorithms for Edge Computing Applications." Mathematics 13, no. 8 (2025): 1308. https://doi.org/10.3390/math13081308.
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Fractional-order systems capture complex dynamic behaviors more accurately than integer-order models, yet their real-time identification remains challenging, particularly in resource-constrained environments. This work proposes a hybrid framework that combines Particle Swarm Optimization (PSO) with various artificial intelligence (AI) techniques to estimate reduced-order models of fractional systems. First, PSO optimizes model parameters by minimizing the discrepancy between the high-order system response and the reduced model output. These optimized parameters then serve as training data for several AI-based algorithms—including neural networks, support vector regression (SVR), and extreme gradient boosting (XGBoost)—to evaluate their inference speed and accuracy. Experimental validation on a custom-built heating system demonstrates that both PSO and the AI techniques yield precise reduced-order models. While PSO achieves slightly lower error metrics, its iterative nature leads to higher and more variable computation times compared to the deterministic and rapid inference of AI approaches. These findings highlight a trade-off between estimation accuracy and computational efficiency, providing a robust solution for real-time fractional-order system identification on edge devices.
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Chen, Wei, Shujia Geng, Xi Chen, Tao Li, Paraskevas Tsangaratos, and Ioanna Ilia. "Prediction of the Height of Water-Conducting Fissure Zone for Shallow-Buried Coal Seams Under Fully Mechanized Caving Conditions in Northern Shaanxi Province." Water 17, no. 3 (2025): 312. https://doi.org/10.3390/w17030312.
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Accurate prediction of the height of water-conducting fissure zone (HWCFZ) is an important issue in coal water control and a prerequisite for ensuring the safe production of coal mines. At present, the prediction model of HWCFZ has some issues such as poor prediction accuracy. Based on the widely collected measured data of the HWCFZ in different coal mines in northern Shaanxi Province, China, the HWCFZ in shallow-buried coal seams is categorized into two types, i.e., typical shallow-buried coal seams and near-shallow-buried seams, according to the different depths of burial and base-loading ratios. On the basis of summarizing the research results of the previous researchers, three factors, namely, mining thickness, coal seam depth, and working length, were selected, and the data of the height of the water-conducting fissure zone in the study area were analyzed by using a multivariate nonlinear regression method. Subsequently, each group of the data was randomly divided into training data and validation data with a ratio of 70:30. Then, the training data were used to build a neural network model (BP), random forest model (RF), a hybrid integration of particle swarm optimization and the support vector machine model (PSO-SVR), and a hybrid integration of genetic algorithm optimization and the support vector machine model (GA-SVR). Finally, the test samples were used to test the model accuracy and evaluate the generalization ability. Accordingly, the optimal prediction model for the typical shallow-buried area and near-shallow-buried area of Jurassic coal seams in northern Shaanxi was established. The results show that the HWCFZ for the typical shallow-buried coal seam is suitable to be determined by the multivariate nonlinear regression method, with an accuracy of 0.64; the HWCFZ for near-shallow-buried coal seams is suitable to be predicted by the two-factor PSO-SVR computational model of mining thickness and the burial depth, with a prediction accuracy of 0.84; and machine learning methods are more suitable for near-shallow-buried areas, dealing with small-scale data and discrete data.
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K. Agrawal, Rajesh, Swati B. Baste, and Yogita S. Rathod. "SWARM INTELLIGENCE-DRIVEN FRAMEWORK FOR PRECISE AND DYNAMIC WEATHER FORECASTING." ICTACT Journal on Soft Computing 16, no. 1 (2025): 3803–7. https://doi.org/10.21917/ijsc.2025.0527.
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Weather prediction plays a vital role in safeguarding life and optimizing resource planning. However, the inherent chaotic nature of atmospheric systems makes precise forecasting a persistent challenge. Traditional numerical and statistical models often lack adaptability and accuracy, especially in rapidly changing weather conditions. These models may not fully leverage the potential of data-driven adaptive intelligence for real-time prediction. This study proposes a novel weather prediction model based on Swarm Intelligence (SI), specifically utilizing a hybrid Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithm. The hybrid SI model is designed to fine-tune predictive parameters dynamically while adapting to spatio-temporal variations in meteorological data. The framework incorporates multi-source weather data (temperature, humidity, pressure, and wind speed) and applies optimized machine learning regression models, whose hyperparameters are tuned through the SI-based approach. The proposed SI model was tested against benchmark datasets using MATLAB simulations. It showed improved prediction accuracy and adaptability compared to existing methods, including ARIMA, Support Vector Regression (SVR), LSTM, and standalone PSO-tuned models. The hybrid SI framework achieved a notable increase in accuracy (6–12%) and reduced prediction error across different climate zones, demonstrating its effectiveness in dynamic conditions.
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Li, Baosheng, and Chuandong Qin. "Predictive Analytics for Octane Number: A Novel Hybrid Approach of KPCA and GS-PSO-SVR Model." IEEE Access 9 (2021): 66531–41. http://dx.doi.org/10.1109/access.2021.3077028.
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Li, Ming-Wei, Wei-Chiang Hong, and Hai-Gui Kang. "Urban traffic flow forecasting using Gauss–SVR with cat mapping, cloud model and PSO hybrid algorithm." Neurocomputing 99 (January 2013): 230–40. http://dx.doi.org/10.1016/j.neucom.2012.08.002.
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Jalalifar, Salman, Mojtaba Masoudi, Rouzbeh Abbassi, Vikram Garaniya, Mohammadmahdi Ghiji, and Fatemeh Salehi. "A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor." Energy 191 (January 2020): 116414. http://dx.doi.org/10.1016/j.energy.2019.116414.
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Xu, Junfei, Junhua Wang, Yanming Wu, et al. "Prediction of Geometric Dimensions of Deposited Layer Produced Using Laser-arc Hybrid Additive Manufacturing." Micromachines 15, no. 7 (2024): 830. http://dx.doi.org/10.3390/mi15070830.
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Laser-arc hybrid additive manufacturing (LAHAM) holds substantial potential in industrial applications, yet ensuring dimensional accuracy remains a major challenge. Accurate prediction and effective control of the geometrical dimensions of the deposited layers are crucial for achieving this accuracy. The width and height of the deposited layers, key indicators of geometric dimensions, directly affect the forming precision. This study conducted experiments and in-depth analysis to investigate the influence of various process parameters on these dimensions and proposed a predictive model for accurate forecasting. It was found that the width of the deposited layers was positively correlated with laser power and arc current and negatively correlated with scanning speed, while the height was negatively correlated with laser power and scanning speed and positively with arc current. Quantitative analysis using the Taguchi method revealed that the arc current had the most significant impact on the dimensions of the deposited layers, followed by scanning speed, with laser power having the least effect. A predictive model based on extreme gradient boosting (XGBoost) was developed and optimized using particle swarm optimization (PSO) for tuning the number of leaf nodes, learning rate, and regularization coefficients, resulting in the PSO-XGBoost model. Compared to models enhanced with PSO-optimized support vector regression (SVR) and XGBoost, the PSO-XGBoost model exhibited higher accuracy, the smallest relative error, and performed better in terms of Mean Relative Error (MRE), Mean Square Error (MSE), and Coefficient of Determination R2 metrics. The high predictive accuracy and minimal error variability of the PSO-XGBoost model demonstrate its effectiveness in capturing the complex nonlinear relationships between process parameters and layer dimensions. This study provides valuable insights for controlling the geometric dimensions of the deposited layers in LAHAM.
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Xu, Jie, Feng Liu, Zhenglei He, Zongao Zhang, and Sheng Li. "Cost optimization of sodium hypochlorite bleaching washing for denim by combining ensemble of surrogates with particle swarm optimization." Journal of Engineered Fibers and Fabrics 16 (January 2021): 155892502110223. http://dx.doi.org/10.1177/15589250211022331.
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Sodium hypochlorite bleaching washing process has been broadly carried out in denim garment industrial production. However, the quantitative relationships between process variables and bleaching performances have not been illustrated explicitly. Hence, it is impractical to determine values of the variables that can achieve the optimal production cost while satisfying the requirements of customers. This paper proposes an optimization methodology by combining ensemble of surrogates (ESs) with particle swarm optimization (PSO) to optimize production cost of chlorine bleaching for denim. The methodology starts from the data collections by conducting a Taguchi L25 (56) orthogonal experiment with the process variables and metrics for evaluating bleaching performances. Based on the data, the quantitative relationships are separately constructed by using RBFNN, SVR, RF and ensemble of them. Then, accuracies of the surrogates are evaluated and it proves that the ESs outperforms the others. Later, the production cost optimization model is proposed and PSO is utilized to solve it, while a case study is given to depict the optimization process and verify the effectiveness of the proposed hybrid ESs-PSO approach. Overall, the ESs-PSO approach shows great capability of optimizing production cost of sodium hypochlorite bleaching washing for denim.
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Zhang, Chenchen, Yilin Cong, Ye Tian, Anzhu Guo, Tao Liu, and Yongzhi Ma. "Research on Combined Forecasting of Cooling Load Based on Advanced Cuckoo Search and Improved Particle Swarm Optimization." Journal of Physics: Conference Series 2160, no. 1 (2022): 012044. http://dx.doi.org/10.1088/1742-6596/2160/1/012044.
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Abstract This study aims to improve the real-time accuracy of cooling load forecasting for heating, ventilating and air-conditioning systems (HVAC). This article takes the cooling load in a study room in Qingdao, China, which has been put into use for the first time, as the research object, and establishes a TRNSYS simulation platform to obtain sufficient load data. After using the mean influence value (MIV) and Spearman correlation coefficient to screen the characteristic variables, a hybrid algorithm (CS-CPSO) based on cuckoo search (CS) and particle swarm optimization (PSO) is proposed. Firstly, the iterative extremum is introduced to PSO, secondly, mechanism of levy random flight to generate random new nest in CS is used to initialize PSO particles adaptively, Finally, the optimization algorithm is applied to optimize the back propagation (BP) and support vector regression (SVR) load training models (WBP, WSVR, RBP, RSVR) of the working day (W) and rest day (R), respectively. The maximum grey correlation coefficient is utilized to establish the both models (CS-CPSO-CW, CS-CPSO-CR) of the working day (W) and rest day (R) based on CS-CPSO. In this way, the forecasting results are optimized and then compared with the regression prediction method. The analysis shows that the accuracy of the optimized BP model and SVR model are improved and fully considering the differences, the accuracy of the cooling load prediction is effectively promoted by separately, optimal selection between the prediction values of advanced models (CS-CPSO-WBP, CS-CPSO-WSVR and CS-CPSO-RBP, CS-CPSO-RSVR) gives full play to each algorithm’s advantages and makes up for their shortcomings, and it greatly increases reliability and improves accuracy, which in turn provides the basis for the optimal plan, control, and operation of the HVAC.
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Vasconcelos, Lia, Luís G. Dias, Ana Leite, et al. "Can Near-Infrared Spectroscopy Replace a Panel of Tasters in Sensory Analysis of Dry-Cured Bísaro Loin?" Foods 12, no. 23 (2023): 4335. http://dx.doi.org/10.3390/foods12234335.
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This study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveiled distinct peaks associated with significant components, such as proteins, lipids and water. Support vector regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization and the radial base kernel (non-linear SVR model). This process involved partitioning the data into calibration (67%) and prediction (33%) subsets using the SPXY algorithm. The model parameters were optimized via a hybrid algorithm based on particle swarm optimization (PSO) to effectively minimize the root-mean-square error (RMSECV) derived from five-fold cross-validation and ensure the attainment of optimal model performance and predictive accuracy. The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616–0.9955) and low RMSE values (0.0400–0.1031). The prediction set’s relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig meat aroma, hardness, fat color and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment. Such advancements have the potential to benefit both the research community and the meat industry by closely aligning their practices with consumer preferences and expectations.
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Qian, Zhang, Rao Jiarui, and Hong Jiaqi. "SeaMNF vs. LDA: Unveiling the Power of Short Text Mining in Financial Markets." International Journal of Engineering and Management Research 14, no. 5 (2024): 76–82. https://doi.org/10.5281/zenodo.14061466.
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The objective of this study is to construct a time series forecasting framework that incorporates textual features. By leveraging text mining techniques, we extract thematic and sentiment information from a vast array of news headlines related to the future. These text-derived features are then utilized as exogenous variables for prediction purposes. This paper addresses two critical questions: why headlines over full articles and why futures news over gold news. News headlines are considered summaries of the full articles, encapsulating most of the essential information. Additionally, our approach aligns with the work of Li et al. [1,2,3,4,5] which opted for news headlines to extract topics and sentiment information. The choice of futures news over gold news is justified by the scarcity of crude oil news and the established complex correlations between futures prices such as gold, natural gas, and crude oil. Research by Sujit & Kumar (2011) suggests that gold price fluctuations can impact the WTI index, and the dependence of different countries on crude oil can influence their currency exchange rates, thereby affecting the purchasing power of gold. Villar & Joutz (2006) indicate that a 20% temporary shock to WTI has a 5% contemporaneous impact on natural gas prices.[6,7,8,9] We construct a daily topic strength index by following the SeaMNF approach, which allows us to calculate the probability of each headline belonging to each topic. The optimal number of topics is selected based on Pointwise Mutual Information (PMI) scores. Given the vast number of news articles published daily by media outlets, we compute the average weight of news as the topic strength for the day. The topic strength index for day t is defined as the sum of the weights of the first topic across all news articles published on that day.[10,11,12,13,14,15]
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Liu, Jingrui, Zhiwen Hou, Bowei Liu, and Xinhui Zhou. "Mathematical and Machine Learning Innovations for Power Systems: Predicting Transformer Oil Temperature with Beluga Whale Optimization-Based Hybrid Neural Networks." Mathematics 13, no. 11 (2025): 1785. https://doi.org/10.3390/math13111785.
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Power transformers are vital in power systems, where oil temperature is a key operational indicator. This study proposes an advanced hybrid neural network model, BWO-TCN-BiGRU-Attention, to predict the top-oil temperature of transformers. The model was validated using temperature data from power transformers in two Chinese regions. It achieved MAEs of 0.5258 and 0.9995, MAPEs of 2.75% and 2.73%, and RMSEs of 0.6353 and 1.2158, significantly outperforming mainstream methods like ELM, PSO-SVR, Informer, CNN-BiLSTM-Attention, and CNN-GRU-Attention. In tests conducted in spring, summer, autumn, and winter, the model’s MAPE was 2.75%, 3.44%, 3.93%, and 2.46% for Transformer 1, and 2.73%, 2.78%, 3.07%, and 2.05% for Transformer 2, respectively. These results indicate that the model can maintain low prediction errors even with significant seasonal temperature variations. In terms of time granularity, the model performed well at both 1 h and 15 min intervals: for Transformer 1, MAPE was 2.75% at 1 h granularity and 2.98% at 15 min granularity; for Transformer 2, MAPE was 2.73% at 1 h granularity and further reduced to 2.16% at 15 min granularity. This shows that the model can adapt to different seasons and maintain good prediction performance with high-frequency data, providing reliable technical support for the safe and stable operation of power systems.
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Chen, Zhi, Wenlong Wang, Zhizuo Li, and Hongzhi Yan. "Modeling and Optimization of the Blade Structural Parameters for a Turbomolecular Pump." Machines 11, no. 5 (2023): 517. http://dx.doi.org/10.3390/machines11050517.
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The development of high-performance mass spectrometer and vacuum coating technology has placed higher demand on the vacuum level of turbomolecular pumps (TMPs), which are required to possess a greater compression ratio and faster pumping speed. There exists a relation of “as one falls, another rises” between the compression ratio and the pumping speed when traditional improvement methods are used. How to simultaneously increase the compression ratio and pumping speed is a very important question for the high-end turbomolecular pumps. In this study, on the basis of a parallel blade and thin gas aerodynamic model, several types of curved blade are presented to improve the pumping performance of TMPs. The comparison results show that the positive quadratic surface exhibited a better pumping performance than the other curved blades. After that, a hybrid optimization method based on a support vector machine (SVR) and particle swarm optimization (PSO) are proposed to obtain the structural parameters of the rotor blade for the highest pumping speed and maximum compression ratio. The optimization results show that, compared with the parallel blades, the compression single-stage blade row with quadratic surface structure was able to increase the maximum compression ratio by 10.35% and the maximum pumping speed factor by 4.61%. In addition, the intermediate single-stage blade row with quadratic surface structure increased the maximum compression ratio by 9.15% and the maximum pumping speed factor by 2.53%.
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Mohan Kumar, T. L., and Prajneshu. "Development of Hybrid Models for Forecasting Time-Series Data Using Nonlinear SVR Enhanced by PSO." Journal of Statistical Theory and Practice 9, no. 4 (2015): 699–711. http://dx.doi.org/10.1080/15598608.2014.977981.
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