Relevant bibliographies by topics / PSO-SVR Hybrid Model

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Academic literature on the topic 'PSO-SVR Hybrid Model'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "PSO-SVR Hybrid Model"

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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 p
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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 impro
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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
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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
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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
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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 a
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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
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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 calle
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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 regressi
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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 pol
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Conference papers on the topic "PSO-SVR Hybrid Model"

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Xia, Hanbing, Ji Han, and Jelena Milisavljevic-Syed. "Predicting the Quantity of Recycled End-of-Life Products Using a Hybrid SVR-Based Model." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-114718.

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Abstract End-of-life product recycling is crucial for achieving sustainability in circular supply chains and improving resource utilization. Forecasting the quantity of recycled end-of-life products is essential for planning and managing reverse supply chain operations. Decision-makers and practitioners can benefit from this information when designing reverse logistics networks, managing tactical disposal, planning capacity, and operational production. To address the challenge of small sample data with multiple factors influencing the recycling number, and to deal with the randomness and nonli
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Tang, Li-Chun, Xiu-juan Xu, and Liang Lu. "Forecast Model of V-SVR Based on an Improved GA-PSO Hybrid Algorithm." In 2012 4th International Conference on Multimedia Information Networking and Security (MINES). IEEE, 2012. http://dx.doi.org/10.1109/mines.2012.114.

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Alves, Emilly Pereira, Joao Fausto Lorenzato Oliveira, Manoel Henrique da Nóbrega Marinho, and Francisco Madeiro. "A Nonlinear Optimizated PSO-SVR Hybrid System for Time Series Forecasting with ARIMA." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-54.

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In the forecasting time series field, the combination of techniques to aid in predicting different patterns has been the subject of several studies. Hybrid models have been widely applied in this scenario, where the vast majority of series are composed of linear and nonlinear patterns. The Autoregressive Integrated Moving Average (ARIMA) presents satisfactory results in a linear pattern prediction but can not capture nonlinear ones. In dealing with nonlinear patterns, the Support Vector Regression (SVR) has shown promising results. In order to map both patterns, an optimized nonlinear combinat
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Pathmanathan, Elangeshwaran, Rosdiazli Ibrahim, and Vijanth Sagayan Asirvadam. "CO2 emission model development employing particle swarm optimized — Least squared SVR (PSO-LSSVR) hybrid algorithm." In 2012 4th International Conference on Intelligent & Advanced Systems (ICIAS). IEEE, 2012. http://dx.doi.org/10.1109/icias.2012.6306175.

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Amzar, Haikal, Meor Irfan Haziq, and Zazilah May. "Hybrid of PSO-ANN and PCA-SVR Models for the Prediction of External Corrosion in Pipeline Infrastructure: A Comparative Study." In 2023 IEEE International Conference on Sensors and Nanotechnology (SENNANO). IEEE, 2023. http://dx.doi.org/10.1109/sennano57767.2023.10352550.

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