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Manfredini, Ricardo Augusto. "Hybrid Artificial Neural Networks for Electricity Consumption Prediction." International Journal of Advanced Engineering Research and Science 9, no. 8 (2022): 292–99. http://dx.doi.org/10.22161/ijaers.98.32.
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We present a comparative study of electricity consumption predictions using the SARIMAX method (Seasonal Auto Regressive Moving Average eXogenous variables), the HyFis2 model (Hybrid Neural Fuzzy Inference System) and the LSTNetA model (Long and Short Time series Network Adapted), a hybrid neural network containing GRU (Gated Recurrent Unit), CNN (Convolutional Neural Network) and dense layers, specially adapted for this case study. The comparative experimental study developed showed a superior result for the LSTNetA model with consumption predictions much closer to the real consumption. The LSTNetA model in the case study had a rmse (root mean squared error) of 198.44, the HyFis2 model 602.71 and the SARIMAX method 604.58.
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Aouatif, Ibnelouad, Elkari Abdeljalil, Ayad Hassan, and Mjahed Mostafa. "A neuro-fuzzy approach for tracking maximum power point of photovoltaic solar system." International Journal of Power Electronics and Drive System (IJPEDS) 12, no. 2 (2021): 1252–64. https://doi.org/10.11591/ijpeds.v12.i2.pp1252-1264.
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This work presents a hybrid soft-computing methodology approach for intelligent maximum power point tracking (MPPT) techniques of a photovoltaic (PV) system under any expected operating conditions using artificial neural network-fuzzy (neuro-fuzzy). The proposed technique predicts the calculation of the duty cycle ensuring optimal power transfer between the PV generator and the load. The neuro-fuzzy hybrid method combines artificial neural network (ANN) to direct the controller to the region where the MPP is located with its reference voltage estimator and its block of neural order. After that, the fuzzy logic controller (FLC) with rule inference begins to establish the photovoltaic solar system at the MPP. The obtained simulation results using MATLAB/simulink software for the proposed approach compared to ANN and the perturb and observe (P&O), proved that neuro-fuzzy approach fulfilled to extract the optimum power with pertinence, efficiency and precision.
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Thakur, Amey. "Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 128–35. http://dx.doi.org/10.22214/ijraset.2021.37930.
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Abstract: Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks and fuzzy logic systems are parameterized nonlinear computing methods for numerical data processing (signals, images, stimuli). These algorithms can be integrated into dedicated hardware or implemented on a general-purpose computer. The network system acquires knowledge through a learning process. Internal parameters are used to store the learned information (weights). Keywords: Artificial Neural Networks (ANNs), Neural Networks (NNs), Fuzzy Logic (FL), Neuro-Fuzzy, Probability Reasoning, Soft Computing, Fuzzification, Defuzzification, Fuzzy Inference Systems, Membership Function.
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Cagcag Yolcu, Ozge. "A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/560472.
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Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.
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Ibnelouad, Aouatif, Abdeljalil Elkari, Hassan Ayad, and Mostafa Mjahed. "A neuro-fuzzy approach for tracking maximum power point of photovoltaic solar system." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 2 (2021): 1252. http://dx.doi.org/10.11591/ijpeds.v12.i2.pp1252-1264.
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This work presents a hybrid soft-computing methodology approach for intelligent maximum power point tracking (MPPT) techniques of a photovoltaic (PV) system under any expected operating conditions using artificial neural network-fuzzy (neuro-fuzzy). The proposed technique predicts the calculation of the duty cycle ensuring optimal power transfer between the PV generator and the load. The neuro-fuzzy hybrid method combines artificial neural network (ANN) to direct the controller to the region where the MPP is located with its reference voltage estimator and its block of neural order. After that, the fuzzy logic controller (FLC) with rule inference begins to establish the photovoltaic solar system at the MPP. The obtained simulation results using MATLAB/simulink software for the proposed approach compared to ANN and the perturb and observe (P&O), proved that neuro-fuzzy approach fulfilled to extract the optimum power with pertinence, efficiency and precision
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Tan, Xiao, Yuan Zhou, Zuohua Ding, and Yang Liu. "Selecting Correct Methods to Extract Fuzzy Rules from Artificial Neural Network." Mathematics 9, no. 11 (2021): 1164. http://dx.doi.org/10.3390/math9111164.
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Artificial neural network (ANN) inherently cannot explain in a comprehensible form how a given decision or output is generated, which limits its extensive use. Fuzzy rules are an intuitive and reasonable representation to be used for explanation, model checking, and system integration. However, different methods may extract different rules from the same ANN. Which one can deliver good quality such that the ANN can be accurately described by the extracted fuzzy rules? In this paper, we perform an empirical study on three different rule extraction methods. The first method extracts fuzzy rules from a fuzzy neural network, while the second and third ones are originally designed to extract crisp rules, which can be transformed into fuzzy rules directly, from a well-trained ANN. In detail, in the second method, the behavior of a neuron is approximated by (continuous) Boolean functions with respect to its direct input neurons, whereas in the third method, the relationship between a neuron and its direct input neurons is described by a decision tree. We evaluate the three methods on discrete, continuous, and hybrid data sets by comparing the rules generated from sample data directly. The results show that the first method cannot generate proper fuzzy rules on the three kinds of data sets, the second one can generate accurate rules on discrete data, while the third one can generate fuzzy rules for all data sets but cannot always guarantee the accuracy, especially for data sets with poor separability. Hence, our work illustrates that, given an ANN, one should carefully select a method, sometimes even needs to design new methods for explanations.
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Yao, Ying Xue, Y. Lu, Zhe Jun Yuan, and J. Y. Hu. "A Hybrid Model for Tool Condition Monitoring and Optimal Tool Management." Materials Science Forum 471-472 (December 2004): 865–70. http://dx.doi.org/10.4028/www.scientific.net/msf.471-472.865.
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This paper introduces a new hybrid model for tool condition monitoring (TCM) and optimal tool management (OTM) in end milling operation. The model includes a wavelet fuzzy neural network with acoustic emission (AE) and a model of fuzzy classification of tool wear state with the detected cutting parameters supported by cutting database. The results estimated by cutting conditions and detected signals are fused by artificial neural network (ANN) so as to facilitate effective tool replacement at a proper state or time. The validity and reliability of the method are verified by experimental results.
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Gennady, Kaniuk, Vasylets Tetiana, Varfolomiyev Oleksiy, Mezerya Andrey, and Antonenko Nataliia. "Development of neuralnetwork and fuzzy models of multimass electromechanical systems." Eastern-European Journal of Enterprise Technologies 3, no. 2(99) (2019): 51–63. https://doi.org/10.15587/1729-4061.2019.169080.
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The study objective was to construct models of multimass electromechanical systems using neural nets, fuzzy inference systems and hybrid networks by means of MATLAB tools. A model of a system in a form of a neural net or a neuro-fuzzy inference system was constructed on the basis of known input signals and signals measured at the system output. Methods of the theory of artificial neural nets and methods of the fuzzy modeling technology were used in the study. A neural net for solving the problem of identification of the electromechanical systems with complex kinematic connections was synthesized using the Neural Network Toolbox application package of the MATLAB system. A possibility of solving the identification problem using an approximating fuzzy system using the Fuzzy Logic Toolbox package was considered. A hybrid network was synthesized and implemented in a form of an adaptive neuro-fuzzy inference system using the ANFIS editor. Recommendations for choosing parameters that have the most significant effect on identification accuracy when applying the methods under consideration were given. It was shown that the use of neural nets and adaptive neuro-fuzzy inference systems makes it possible to identify systems with accuracy of 2 to 4%. As a result of the conducted studies, efficiency of application of neural nets, fuzzy inference systems and hybrid nets to identification of systems with complex kinematic connections in the presence of "input-output" information was shown. The neural-network, fuzzy and neuro-fuzzy models of two-mass electromechanical systems were synthesized with the use of modern software tools. The considered approach to using artificial intelligence technologies, that is neural nets and fuzzy logic is a promising line of construction of appropriate neural-network and neuro-fuzzy models of technical objects and systems. The study results can be used in synthesis of regulators for the systems with complex kinematic connections to ensure their high performance.
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Tselentis, G.-A., and E. Sokos. "Relationship between isoseismal area and magnitude of historical earthquakes in Greece by a hybrid fuzzy neural network method." Natural Hazards and Earth System Sciences 12, no. 1 (2012): 37–45. http://dx.doi.org/10.5194/nhess-12-37-2012.
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Abstract. In this paper we suggest the use of diffusion-neural-networks, (neural networks with intrinsic fuzzy logic abilities) to assess the relationship between isoseismal area and earthquake magnitude for the region of Greece. It is of particular importance to study historical earthquakes for which we often have macroseismic information in the form of isoseisms but it is statistically incomplete to assess magnitudes from an isoseismal area or to train conventional artificial neural networks for magnitude estimation. Fuzzy relationships are developed and used to train a feed forward neural network with a back propagation algorithm to obtain the final relationships. Seismic intensity data from 24 earthquakes in Greece have been used. Special attention is being paid to the incompleteness and contradictory patterns in scanty historical earthquake records. The results show that the proposed processing model is very effective, better than applying classical artificial neural networks since the magnitude macroseismic intensity target function has a strong nonlinearity and in most cases the macroseismic datasets are very small.
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Salais-Fierro, Tomas Eloy, Jania Astrid Saucedo-Martinez, Roman Rodriguez-Aguilar, and Jose Manuel Vela-Haro. "Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry." Applied Sciences 10, no. 3 (2020): 829. http://dx.doi.org/10.3390/app10030829.
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According to the literature review performed, there are few methods focused on the study of qualitative and quantitative variables when making demand projections by using fuzzy logic and artificial neural networks. The purpose of this research is to build a hybrid method for integrating demand forecasts generated from expert judgements and historical data and application in the automotive industry. Demand forecasts through the integration of variables; expert judgements and historical data using fuzzy logic and neural network. The methodology includes the integration of expert and historical data applying the Delphi method as a means of collecting fuzzy date. The result according to proposed methodology shows how fuzzy logic and neural networks is an alternative for demand planning activity. Machine learning techniques are techniques that generate alternatives for the tools development for demand forecasting. In this study, qualitative and quantitative variables are integrated through the implementation of fuzzy logic and time series artificial neural networks. The study aims to focus in manufacturing industry factors in conjunction time series data.
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Souza, Paulo Vitor de Campos, Augusto Junio Guimaraes, Vanessa Souza Araujo, and Edwin Lughofer. "An intelligent Bayesian hybrid approach to help autism diagnosis." Soft Computing 25, no. 14 (2021): 9163–83. http://dx.doi.org/10.1007/s00500-021-05877-0.
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AbstractThis paper proposes a Bayesian hybrid approach based on neural networks and fuzzy systems to construct fuzzy rules to assist experts in detecting features and relations regarding the presence of autism in human beings. The model proposed in this paper works with a database generated through mobile devices that deals with diagnoses of autistic characteristics in human beings who answer a series of questions in a mobile application. The Bayesian model works with the construction of Gaussian fuzzy neurons in the first and logical neurons in the second layer of the model to form a fuzzy inference system connected to an artificial neural network that activates a robust output neuron. The new fuzzy neural network model was compared with traditional state-of-the-art machine learning models based on high-dimensional based on real-world data sets comprising the autism occurrence in children, adults, and adolescents. The results (97.73- Children/94.32-Adolescent/97.28-Adult) demonstrate the efficiency of our new method in determining children, adolescents, and adults with autistic traits (being among the top performers among all ML models tested), can generate knowledge about the dataset through fuzzy rules.
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Sikka, Rishi, Hetan Chaudhary, Mansingh Meena, and M. N. Nachappa. "Implementing an Adaptive Algorithm for Hybrid EVs: Recognising Driving Patterns with Artificial Intelligence." E3S Web of Conferences 540 (2024): 02020. http://dx.doi.org/10.1051/e3sconf/202454002020.
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This review article delves into the enhancement of fuel efficiency in hybrid electric vehicles (HEVs) through the use of adaptive algorithms for precise driving pattern recognition. The review explores studies that delve into two distinct methodologies. Firstly, a method utilising a Learning Vector Quantisation neural network is highlighted, which analyses six standard driving cycles. By employing micro-trip extraction and Principal Component Analysis, this method ensures a comprehensive training sample, subsequently simplifying the model and reducing data convergence time. Simulations reveal a significant reduction in sampling duration whilst maintaining satisfactory accuracy, leading to an 8% improvement in fuel economy when paired with a parallel hybrid vehicle model. Additionally, the article examines the Neural Network Fuzzy Energy Management Strategy (NNF-EMS), designed to address the adaptability constraints of traditional energy management strategies. Through neural network learning and parameter analysis, the NNF-EMS showcases enhanced adaptability and practicality across diverse driving cycles, underscoring the potential of artificial intelligence in HEV algorithm development..
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Wu, Xinhao, and Qiujun Lu. "Financial asset yield series forecasting based on risk-neutral fuzzy bilinear regression and probabilistic neural network." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 11829–44. http://dx.doi.org/10.3233/jifs-202927.
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Application of quantitative methods for forecasting purposes in financial markets has attracted significant attention from researchers and managers in recent years when conventional time series forecasting models can hardly develop the inherent rules of complex nonlinear dynamic financial systems. In this paper, based on the fuzzy technique integrated with the statistical tools and artificial neural network, a new hybrid forecasting system consisting of three stages is constructed to exhibit effectively improved forecasting accuracy of financial asset price. The sum of squared errors is minimized to determine the coefficients in fitting the fuzzy autoregression model stage for formulating sample groups to deal with data containing outliers. Fuzzy bilinear regression model introducing risk view based on quadratic programming algorithm that reflects the properties of both least squares and possibility approaches without expert knowledge is developed in the second stage. The main idea of the model considers the sub-models tracking the possible relations between the spread and the center, also linking the estimation deviation with risk degree of fitness of the model. In the third stage, fuzzy bilinear regression forecasting combining with the optimal architecture of probabilistic neural network classifiers indicates that the proposed method has great contribution to control over-wide interval financial data with a certain confidence level. Statistical validation and performance analysis using historical financial asset yield series on Shanghai Stock Exchange composite index all exhibit the effectiveness and stability of the proposed hybrid forecasting formulation compared with other forecasting methods.
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Golubinskiy, Andrey, and Andrey Tolstykh. "Hybrid method of conventional neural network training." Informatics and Automation 20, no. 2 (2021): 463–90. http://dx.doi.org/10.15622/ia.2021.20.2.8.
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The paper proposes a hybrid method for training convolutional neural networks. The method consists of combining second and first-order methods for different elements of the architecture of a convolutional neural network. The hybrid convolution neural network training method allows to achieve significantly better convergence compared to Adam; however, it requires fewer computational operations to implement. Using the proposed method, it is possible to train networks on which learning paralysis occurs when using first-order methods. Moreover, the proposed method could adjust its computational complexity to the hardware on which the computation is performed; at the same time, the hybrid method allows using the mini-packet learning approach.
 The analysis of the ratio of computations between convolutional neural networks and fully connected artificial neural networks is presented. The mathematical apparatus of error optimization of artificial neural networks is considered, including the method of backpropagation of the error, the Levenberg-Marquardt algorithm. The main limitations of these methods that arise when training a convolutional neural network are analyzed.
 The analysis of the stability of the proposed method when the initialization parameters are changed. The results of the applicability of the method in various problems are presented.
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Chau, K. W., and C. L. Wu. "A hybrid model coupled with singular spectrum analysis for daily rainfall prediction." Journal of Hydroinformatics 12, no. 4 (2010): 458–73. http://dx.doi.org/10.2166/hydro.2010.032.
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A hybrid model integrating artificial neural networks and support vector regression was developed for daily rainfall prediction. In the modeling process, singular spectrum analysis was first adopted to decompose the raw rainfall data. Fuzzy C-means clustering was then used to split the training set into three crisp subsets which may be associated with low-, medium- and high-intensity rainfall. Two local artificial neural network models were involved in training and predicting low- and medium-intensity subsets whereas a local support vector regression model was applied to the high-intensity subset. A conventional artificial neural network model was selected as the benchmark. The artificial neural network with the singular spectrum analysis was developed for the purpose of examining the singular spectrum analysis technique. The models were applied to two daily rainfall series from China at 1-day-, 2-day- and 3-day-ahead forecasting horizons. Results showed that the hybrid support vector regression model performed the best. The singular spectrum analysis model also exhibited considerable accuracy in rainfall forecasting. Also, two methods to filter reconstructed components of singular spectrum analysis, supervised and unsupervised approaches, were compared. The unsupervised method appeared more effective where nonlinear dependence between model inputs and output can be considered.
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Ganesh, Aman, Ratna Dahiya, and Girish Kumar Singh. "Wide area adaptive hybrid fuzzy STATCOM controller for dynamic stability enhancement." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 35, no. 5 (2016): 1830–49. http://dx.doi.org/10.1108/compel-07-2015-0249.
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Purpose The purpose of this paper is to develop an adaptive fuzzy controller for STATCOM to damp low-frequency inter-area oscillation over wide operating range using wide area signals in multimachine power system. Design/methodology/approach In this paper tuneable fuzzy model is proposed where the parameters of the fuzzy inference system are tuned by using the adaptive characteristic of the artificial neural network. Based on back propagation algorithm and method of least square estimation, the fuzzy inference rule base is tweaked according to the data from which they are modelled. The wide area control signals, for the proposed controller, available in the power system are selected on the basis of eigenvalue sensitivity defined in terms of participation factor. Findings The effectiveness of the proposed controller with wide area signals is tested on two test cases, namely, two area network and IEEE 12 bus benchmark system. The comparative analysis of the proposed adaptive fuzzy controller is carried out with conventional STATCOM controller along with fuzzy-and neural-based supplementary controller all using selected wide area signals. The results show that neural network tuned fuzzy controller leads to better system identification and have enhanced damping characteristics over wide operating range. Originality/value In the available literature, numerous researchers have indicated the use of fuzzy logic controller and neural controller along with their hybrid schemes as STATCOM controller for improving the dynamics of the multimachine power system using local signals. The main contribution of the paper is in using the hybrid intelligent control scheme for STATCOM using wide area signals. The advantage of proposed scheme is that the performance of well-designed fuzzy system can be enhanced with the same training data that are used for designing a neural controller thus giving enhanced performance in comparison to individual intelligent control scheme.
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Plawiak, Pawel, and Ryszard Tadeusiewicz. "Approximation of phenol concentration using novel hybrid computational intelligence methods." International Journal of Applied Mathematics and Computer Science 24, no. 1 (2014): 165–81. http://dx.doi.org/10.2478/amcs-2014-0013.
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Abstract This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg-Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.
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Bazhinov, Oleksiy, Juraj Gerlici, Oleksandr Kravchenko, et al. "Development of a Method for Evaluating the Technical Condition of a Car’s Hybrid Powertrain." Symmetry 13, no. 12 (2021): 2356. http://dx.doi.org/10.3390/sym13122356.
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The article presents the results of a study performed and substantiated based on the principles of a new method of diagnostics of technical conditions of a hybrid powertrain regardless of the structural diagram and design features of a hybrid vehicle. The presented new technology of the diagnostics of hybrid powertrains allows an objective complex assessment of their technical condition by diagnostic parameters in contrast to existing diagnostic methods. In the proposed method, a mechanism for the general standardization of diagnostic parameters has been developed as well as for determining the numerical values of the parameters of the powertrain. The control subset was used to control the learning error. As a result of debugging the system, the scatter of experimental and calculated points has decreased, which confirms the quality of debugging the tested fuzzy model. As a result of training the artificial neural network, the standard deviation of the error in the control sample was 0.012·Pk. A symmetry method of diagnostics of the technical state of a hybrid propulsion system was developed based on the concept of a neural network together with a neuro-fuzzy control with an adaptive criteria based on the method of training a neural network with reinforcement. The components of the vector functional include the criteria for control accuracy, the use of traction battery energy, and the degree of toxicity of exhaust gases. It is proposed to use the principle of symmetry of the guaranteed result and the linear inversion of the vector criterion into a supercriterion to determine the technical state of a hybrid powertrain on a set of Pareto-optimal controls under unequal conditions of optimality.
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Sharma, Rajeev, Binit Kumar Jha, and Vipin Pahuja. "Modeling of process parameters on DSS 2205 through RSM, ANN, fuzzy under cryo-MQL process." Journal of Physics: Conference Series 2484, no. 1 (2023): 012039. http://dx.doi.org/10.1088/1742-6596/2484/1/012039.
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Abstract Duplex stainless steel (DSS) 2205 is considered difficult to cut machine materials. Therefore, an advanced cooling technique, such as a hybrid Liquid carbon dioxide (LCO2)-Minimum quantity of lubrication (MQL) approach, is proposed for drilling duplex stainless steel 2205. In this research study, parametric optimization was performed by using the Response surface method (RSM) with the box-Behnken design of the matrix. For parametric optimization, select three input parameters: drill diameter, spindle speed, and feed rate, while hole deviation and cylindricity error are output responses. The Analysis of variances (ANOVA) test showed that spindle speed is the major factor that influences both responses, e.g., hole deviation and cylindricity error, with contributions of 38.90% and 59.42%, respectively. The modeling and predictive abilities of developed Fuzzy logic system (FLS) and Artificial neural network (ANN) to the comparison of experimental results. It was also found that the predicted values from regression analysis, artificial neural networks, fuzzy logic interfaces, and the proposed method were in good agreement with actual experiment results. Also, the Artificial neural network model provides a minimum % of error of 1.36% and 2.44 %, respectively responses compared to other models.
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Durgasukumar, Gadwala, Repana Ramanjan Prasad, and Srinivasa Rao Gorantla. "A review on soft computing techniques used in induction motor drive application." International Journal of Power Electronics and Drive Systems (IJPEDS) 15, no. 2 (2024): 753. http://dx.doi.org/10.11591/ijpeds.v15.i2.pp753-768.
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<div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>In this paper, hybrid models based on fuzzy systems and neural networks are reviewed. A fuzzy inference system is explicitly represented by expertise for induction motor drives, incorporating the learning capability of artificial neural networks. Researchers have been attracted to neuro-fuzzy techniques for training and inference in induction motor drives due to their efficiency. According to the classification of research articles from 2000 to 2020, this article presents a review of different artificial neural network techniques, fuzzy and neuro-fuzzy systems. The main objective is to provide a concise overview of current neuro-fuzzy research and to enable readers to identify appropriate methods according to their research interests.</p><p> </p></td></tr></tbody></table></div>
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Akbal, Bahadır. "Optimum Cable Bonding with Pareto Optimal and Hybrid Neural Methods to Prevent High-Voltage Cable Insulation Faults in Distributed Generation Systems." Processes 12, no. 12 (2024): 2909. https://doi.org/10.3390/pr12122909.
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The high voltage, current and harmonic distortion in high-voltage cable metal sheaths cause cable insulation faults. The SSBLR (Sectional Solid Bonding with Inductance (L) and Resistance) method was designed as a new cable grounding method to prevent insulation faults. SSBLR was optimized using multi-objective optimization (MOP) with the prediction method (PM) to minimize these factors. The Pareto optimal method was used for MOP. The artificial neural network, hybrid artificial neural network and regression methods were used as the PM. When the artificial neural network–genetic algorithm hybrid method was used as the PM, and the genetic algorithm was used as the optimization method, the voltage and current were significantly reduced in the metal sheath of the cable.
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Babu, Jarapala Ramesh, M. V. D. S. Krishna Murty, Deva Rajashekar, and N. Susheela. "ANN-Based Design of a Hybrid Renewable Energy System for Hybrid Electric Vehicle Applications." E3S Web of Conferences 616 (2025): 01004. https://doi.org/10.1051/e3sconf/202561601004.
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This article examines energy management strategies for hybrid power systems, leveraging an Artificial Neural Network (ANN) to optimize power flow based on real-time needs. The ANN controller ensures maximum power point tracking (MPPT) from renewable sources like photovoltaics (PV), wind turbines (WTs), and fuel cells (FCs), utilizing DC-DC converters. By regulating power flow and dampening fluctuations, the ANN-based method is tested on hybrid setups with PV panels, WTs, and FCs. Simulations in MATLAB/Simulink show that ANN outperforms Fuzzy Logic in MPPT efficiency, particularly for standalone and grid applications at variable loads, enhancing renewable energy reliability.
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R. Abdulhussien, Wijdan. "Hybrid Expert System for Wheat Diseases Diagnosis Using Fuzzy Logic, Neural Network and Bayesian Method." University of Thi-Qar Journal of Science 5, no. 2 (2015): 80–88. http://dx.doi.org/10.32792/utq/utjsci/v5i2.126.
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Expert system is a branch of Artificial Intelligence is a collection of programs which has the ability to reason,justify and answer the queries in a particular domain as a human expert would do. It can be applied to various fields. This research was designed hybrid expert system for the diseases diagnosis of wheat rust by incorporating application of fuzzy logic, neural networks and Bayesian method. The research aim is to tackling the control and remedial measures for disease management for the wheat diseases. The expert system is intended to help the farmers, researchers and students and provides an efficient goal-oriented approach for solving common problems of wheat rust. The system gives results that are correct and consistent.
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Dmitriev, Stepan A., and Alexandra I. Khalyasmaa. "Power Equipment Technical State Assessment Principles." Applied Mechanics and Materials 492 (January 2014): 531–35. http://dx.doi.org/10.4028/www.scientific.net/amm.492.531.
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This article is devoted to the principles of power equipment technical state assessment at 35-220 kV substations. The article deals with the network hybrid model construction using methods of fuzzy logic and artificial neural network. Finally, in order to construct the knowledge base, a methodology of the power equipment technical state assessment, based on the membership functions, is introduced.
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Ashok, R., L. Poovazhagan, S. Srinath Ramkumar, and S. Vignesh Kumar. "Optimization of Material Removal Rate in Wire-EDM Using Fuzzy Logic and Artifical Neural Network." Applied Mechanics and Materials 867 (July 2017): 73–80. http://dx.doi.org/10.4028/www.scientific.net/amm.867.73.
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Present work aims to develop a model and optimize the material removal rate (MRR) in complex wire electric discharge machining (EDM) process. Initially various percentages of aluminium alloy hybrid Nanocomposites were fabricated by novel ultrasonication method. The sample specimens were cut and machined using wire EDM. Experiments were carried out using Taguchi’s L18 orthogonal array under different cutting parameters like Pulse-on, Pulse- off, current and servo voltage. Fuzzy-based Taguchi method and artificial neural network (ANN) with back propagation algorithm were used to optimize the material removal rate. Both ANN and fuzzy logic based models were developed in MATLAB software and the models were trained for estimating the MRR and improving the machining parameter.
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Karthika, J., R. Rajkumar, A. Radhika, and S. Sivaranjani. "Enhancing the Power Tracking Method in Hybrid System using Artificial Neural Network Integrated with Fuzzy Controller." Indian Journal of Science and Technology 12, no. 17 (2019): 1–6. http://dx.doi.org/10.17485/ijst/2019/v12i17/144466.
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Tunç, Taner. "A New Hybrid Method Logistic Regression and Feedforward Neural Network for Lung Cancer Data." Mathematical Problems in Engineering 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/241690.
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Logistic regression (LR) is a conventional statistical technique used for data classification problem. Logistic regression is a model-based method, and it uses nonlinear model structure. Another technique used for classification is feedforward artificial neural networks. Feedforward artificial neural network is a data-based method which can model nonlinear models through its activation function. In this study, a hybrid approach of model-based logistic regression technique and data-based artificial neural network was proposed for classification purposes. The proposed approach was applied to lung cancer data, and obtained results were compared. It was seen that the proposed hybrid approach was superior to logistic regression and feedforward artificial neural networks with respect to many criteria.
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Zaychenko, Yuriy, Helen Zaychenko, and Galib Hamidov. "Hybrid GMDH deep learning networks – analysis, optimization and applications in forecasting at financial sphere." System research and information technologies, no. 1 (April 25, 2022): 73–86. http://dx.doi.org/10.20535/srit.2308-8893.2022.1.06.
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In this paper, the new class of deep learning (DL) neural networks is considered and investigated — so-called hybrid DL networks based on self-organization method Group Method of Data Handling (GDMH). The application of GMDH enables not only to train neural weights, but also to construct the network structure as well. Different elementary neurons with two inputs may be used as nodes of this structure. So the advantage of such a structure is the small number of tuning parameters. In this paper, the optimization of parameters and the structure of hybrid neo-fuzzy networks was performed. The application of hybrid Dl networks for forecasting market indices was considered with various forecasting intervals: one day, one week, and one month. The experimental investigations of hybrid GMDH neo-fuzzy networks were carried out and comparison of its efficiency with FNN ANFIS in the forecasting problem was performed which enabled to estimate their efficiency and advantages.
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Eskandari, Sina, Behrooz Arezoo, and Amir Abdullah. "Thermal Errors Modeling of a CNC Machine’s Axis Using Neural Network and Fuzzy Logic." Applied Mechanics and Materials 110-116 (October 2011): 2976–82. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.2976.
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Thermal errors of CNC machines have significant effects on precision of a workpiece. One of the approaches to reduce these errors is modeling and on-line compensating them. In this study, thermal errors of an axis of the machine are modeled by means of artificial neural networks along with fuzzy logic. Models are created using experimental data. In neural networks modeling, MLP type which has 2 hidden layers is chosen and it is trained by backpropagation algorithm. Finally, the model is validated with the aid of calculating mean squared error and correlation coefficients between outputs of the model and a checking data set. On the other hand, an adaptive neuro-fuzzy inference system is utilized in fuzzy modeling which uses neural network to develop membership functions as fuzzifiers and defuzzifiers. This network is trained by hybrid algorithm. At the end, model validation is done by mean squared error like previous method. The results show that the errors of both modeling techniques are acceptable and models can predict thermal errors reliably.
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Hemamalini, S., and Om Prakash Sharma. "Hybrid structures-based face recognition method using artificial neural network." International Journal of Applied Research on Information Technology and Computing 13, no. 1and2 (2022): 12–23. http://dx.doi.org/10.5958/0975-8089.2022.00002.1.
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ZANCHETTIN, CLEBER, and TERESA B. LUDERMIR. "HYBRID NEURAL SYSTEMS FOR PATTERN RECOGNITION IN ARTIFICIAL NOSES." International Journal of Neural Systems 15, no. 01n02 (2005): 137–49. http://dx.doi.org/10.1142/s0129065705000141.
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This work examines the use of Hybrid Intelligent Systems in the pattern recognition system of an artificial nose. The connectionist approaches Multi-Layer Perceptron and Time Delay Neural Networks, and the hybrid approaches Feature-Weighted Detector and Evolving Neural Fuzzy Networks were investigated. A Wavelet Filter is evaluated as a preprocessing method for odor signals. The signals generated by an artificial nose were composed by an array of conducting polymer sensors and exposed to two different odor databases.
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Aryasatya, Lintang, and Yuliant Sibaroni. "Hate Speech Hashtag Classification Using Hybrid Artificial Neural Network (ANN) Method." JURIKOM (Jurnal Riset Komputer) 9, no. 4 (2022): 784. http://dx.doi.org/10.30865/jurikom.v9i4.4425.
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Social networking sites Twitter is frequently used as a platform for information gathering various communities/forums as well as individuals to discuss certain things. Dissemination of information on Twitter can be in the form of positive information and negative information. One of the negative information is hate speech contained in the form of hashtags on twitter. Hate Speech Hashtag Classification was be carried out using the Hybrid Artificial Neural Network (ANN) method to produce satisfactory results compared to previous methods such as KNN and so on because the large amount of data in Twitter will be very profitable and produce good accuracy when using Hybrid Learning, Hybrid Learning with 5 Cross Validation the highest accuracy is 79% , the lowest is 73%, the average accuracy is 76%.
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Huang, Wei, Sung-Kwun Oh, and Witold Pedrycz. "Hybrid fuzzy polynomial neural networks with the aid of weighted fuzzy clustering method and fuzzy polynomial neurons." Applied Intelligence 46, no. 2 (2016): 487–508. http://dx.doi.org/10.1007/s10489-016-0844-5.
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Chaudhari, Sachin Vasant, Sasikala T.S., Gnanamurthy R.K., Vijay Kumar Dwivedi, and Davinder Kumar. "OPTIMIZING PLANT DISEASE PREDICTION: A NEURO-FUZZY-GENETIC ALGORITHM APPROACH." ICTACT Journal on Soft Computing 14, no. 2 (2023): 3200–3205. http://dx.doi.org/10.21917/ijsc.2023.0448.
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In this essay, the idea of improving plant disease prediction using a Neuro-Fuzzy-Genetic algorithm (NFGA) technique is explored. The concept of a Neuro-Fuzzy-Genetic algorithm is first described. The advantages of the NFGA technique for predicting plant disorders are next addressed. An example is then given to demonstrate how this technique has been successfully used and the advantages it offers you. A hybrid artificial intelligence technique known as a Neuro-Fuzzy-Genetic set of rules (NFGA) combines the genetic algorithms, fuzzy logic, and neural network algorithms. It entails using a method of organizing fuzzy rules for statistics type, developing a network of neurons to anticipate the level of the group of data points to positive fuzzy training, adjusting the weights of fuzzy classes using a genetic algorithm-based totally optimization method to better fit the data factors, and ultimately identifying and predicting patterns of statistics points. The main benefits of this method for predicting plant diseases are its abilities to analyze various plant characteristics, extract complex relationships between statistics points, identify correlations between various environmental factors and illnesses, choose the best combinations of fuzzy rules for accurate classification, and finally adapt to changing data over time.
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Zhao, Ying, Mohammad Noori, Wael A. Altabey, and Naiwei Lu. "Reliability Evaluation of a Laminate Composite Plate Under Distributed Pressure Using a Hybrid Response Surface method." International Journal of Reliability, Quality and Safety Engineering 24, no. 03 (2017): 1750013. http://dx.doi.org/10.1142/s0218539317500139.
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The inherent variability of major infrastructure can be associated with structural properties such as member size and geometry, elastic constants, density, strength characteristics or external load types. These variables and factors may give rise to risk, safety and uncertainty for general structures. In this paper, a comprehensive reliability evaluation framework is presented for a laminate composite plate under hydrostatic pressure. An establishment and verification of a response surface, the determination of performance function in terms of input and output random variables, and the comparative application of combined algorithms such as Monte Carlo simulation, artificial neural network and fuzzy theory are conducted.
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Yunus, M., Markhaban Siswanto, S. Imam Wahyudi, and Soedarsono. "Hybrid System PSO-ANFIS for Optimization of The Water Level in The Tank." IOP Conference Series: Earth and Environmental Science 1321, no. 1 (2024): 012050. http://dx.doi.org/10.1088/1755-1315/1321/1/012050.
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Abstract The speed and pressure of the water flow are determined by the height and volume of the water. The speed of the water flow in the actuator is determined by the use of this flow sensor system. A good tank-based water flow control model should be developed. At a certain point, the actuator stabilizes the rate of water production per minute. Therefore, it is necessary to develop an automatic and precise control technique. Many Artificial Intelligence (AI) methods are used in system optimization. Among them are Particle Swarm Optimization (PSO), Neural Network (NN), Fuzzy Inference System (FIS), and ANFIS. Adaptive Neuro Fuzzy Inference System (ANFIS) is a combination of NN and FIS. In this study, the PSO method was combined with ANFIS. This hybrid method produces better optimization compared to the previous method. The best water level control simulation results are PSO-ANFIS with an overshot of 0.572 pu, undershot of 0.563 pu, and flow output overshot of 0.008 pu, undershot of 0.009 pu.
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Arrar, Sara, and Li Xioaning. "Energy Management in Hybrid Microgrid using Artificial Neural Network, PID, and Fuzzy Logic Controllers." European Journal of Electrical Engineering and Computer Science 6, no. 2 (2022): 38–47. http://dx.doi.org/10.24018/ejece.2022.6.2.414.
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Microgrids are described as linking many power sources (renewable energy and traditional sources) to meet the load consumption in real-time. Because renewable energy sources are intermittent, battery storage systems are required, typically used as a backup system. Indeed, an energy management strategy (EMS) is required to govern power flows across the entire Microgrid. In recent research, various methods have been proposed for controlling the micro-grids, especially voltage and frequency control. This study introduces a microgrid system, an overview of local control in Microgrid, and an efficient EMS for effective microgrid operations using three smart controllers for optimal microgrid stability. We design the Microgrid, which is made up of renewable solar generators and wind sources, Li-ion battery storage system, backup electrical grids, and AC/DC loads, taking into account all of the functional needs of a microgrid EMS and microgrid stability. In addition, the battery energy storage is managed through the performance control of battery charging and discharging using an efficiency controller. The proposed system control is based on the optimum supply of loads through the available renewable sources and the battery State of Charge (SOC). The simulation results using Matlab Simulink show the performance of the three techniques (PID, ANN, and FL) proposed for microgrid stability.
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Li, Yaning, Hongsheng Li, Baoguo Yu, and Jun Li. "A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN." Future Internet 14, no. 8 (2022): 235. http://dx.doi.org/10.3390/fi14080235.
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At present, the interaction mechanism between the complex indoor environment and pseudolite signals has not been fundamentally resolved, and the stability, continuity, and accuracy of indoor positioning are still technical bottlenecks. In view of the shortcomings of the existing indoor fingerprint positioning methods, this paper proposes a hybrid CSI fingerprint method for indoor pseudolite positioning based on Ray Tracing and artificial neural network (RT-ANN), which combines the advantages of actual acquisition, deterministic simulation, and artificial neural network, and adds the simulation CSI feature parameters generated by modeling and simulation to the input of the neural network, extending the sample features of the neural network input dataset. Taking an airport environment as an example, it is proved that the hybrid method can improve the positioning accuracy in the area where the fingerprints have been collected, the positioning error is reduced by 54.7% compared with the traditional fingerprint positioning method. It is also proved that preliminary positioning can be completed in the area without fingerprint collection.
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V., M. Pakhomova, and S. Mandybura Y. "OPTIMAL ROUTE DEFINITION IN THE RAILWAY INFORMATION NETWORK USING NEURAL-FUZZY MODELS." Science and Transport Progress. Bulletin of Dnipropetrovsk National University of Railway Transport, no. 5(83) (November 20, 2019): 81–98. https://doi.org/10.15802/stp2019/184385.
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<strong>Purpose. </strong>Modern algorithms for choosing the shortest route, for example, the Bellman-Ford and Dijkstra algorithms, which are currently widely used in existing routing protocols (RIP, OSPF), do not always lead to an effective result. Therefore, there is a need to study the possibility of organizing routing in in the railway network of information and telecommunication system (ITS) using the methods of artificial intelligence. <strong>Methodology.</strong> On the basis of the simulation model created in the OPNET modeling system a fragment of the ITS railway network was considered and the following samples were formed: training, testing, and control one. For modeling a neural-fuzzy network (hybrid system) in the the MatLAB system the following parameters are input: packet length (three term sets), traffic intensity (five term sets), and the number of intermediate routers that make up the route (four term sets). As the resulting characteristic, the time spent by the packet in the routers along its route in the ITS network (four term sets) was taken. On the basis of a certain time of packet residence in the routers and queue delays on the routers making up different paths (with the same number of the routers) the optimal route was determined. <strong>Findings. </strong>For the railway ITS fragment under consideration, a forecast was made of the packet residence time in the routers along its route based on the neural-fuzzy network created in the MatLAB system. The authors conducted the study of the average error of the neural-fuzzy network`s training with various membership functions and according to the different methods of training optimization. It was found that the smallest value of the average learning error is provided by the neuro-fuzzy network configuration 3–12–60–60–1 when using the symmetric Gaussian membership function according to the hybrid optimization method. <strong>Originality</strong>. According to the RIP and OSPF scenarios, the following characteristics were obtained on the simulation model created in the OPNET simulation system: average server load, average packet processing time by the router, average waiting time for packets in the queue, average number of lost packets, and network convergence time. It was determined that the best results are achieved by the simulation network model according to the OSPF scenario. The proposed integrated routing system in the ITS network of railway transport, which is based on the neural-fuzzy networks created, determines the optimal route in the network faster than the existing OSPF routing protocol. <strong>Practical value. </strong>An integrated routing system in the ITS system of railway transport will make it possible to determine the optimal route in the network with the same number of the routers that make up the packet path in real time.
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Ruiz Hurtado, Andres Felipe, Viviana Vargas-Franco, and Luis Octavio González-Salcedo. "Neural Networks and Fuzzy Logic-Based Approaches for Precipitation Estimation: A Systematic Review." Ingeniería e Investigación 44, no. 3 (2025): e108609. https://doi.org/10.15446/ing.investig.108609.
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Precipitation estimation at the river basin level is essential for watershed management, the analysis of extreme events and weather and climate dynamics, and hydrologic modeling. In recent years, new approaches and tools such as artificial intelligence techniques have been used for precipitation estimation, offering advantages over traditional methods. Two major paradigms are artificial neural networks and fuzzy logic systems, which can be used in a wide variety of configurations, including hybrid and modular models. This work presents a literature review on hybrid metaheuristic and artificial intelligence models based on signal processes, focusing on the applications of these techniques in precipitation analysis and estimation. The selection and comparison criteria used were the model type, the input and output variables, the performance metrics, and the fields of application. An increase in the number of this type of studies was identified, mainly in applications involving neural network models, which tend to get more sophisticated according to the availability and quality of training data. On the other hand, fuzzy logic models tend to hybridize with neural models. There are still challenges related to prediction performance and spatial and temporal resolution at the basin and micro-basin levels, but, overall, these paradigms are very promising for precipitation analysis.
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Dalela, Shaive, Aditya Verma, and A. L.Amutha. "Survey on short-term load forecasting using hybrid neural network techniques." International Journal of Engineering & Technology 7, no. 2.8 (2018): 464. http://dx.doi.org/10.14419/ijet.v7i2.8.10486.
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Load forecasting is an issue of great importance for the reliable operation of the electric power system grids. Various forecasting methodologies have been proposed in the international research bibliography, following different models and mathematical approaches. In the current work, several latest methodologies based on artificial neural networks along with other techniques have be discussed, in order to obtain short-term load forecasting. In this paper, approaches taken by different researchers considering different parameters in means of predicting the lease error has been shown. The paper investigates the application of artificial neural networks (ANN) with fuzzy logic (FL), Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Support Vector Machines(SVM) as forecasting tools for predicting the load demand in short term category. The extracted outcomes indicate the effectiveness of the proposed method, reducing the relative error between real and theoretical data
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Sharma, Sarika, and Smarajit Ghosh. "Effective design and development of hybrid ABC-CSO-based capacitor placement with load forecasting based on artificial neural network." Assembly Automation 39, no. 5 (2019): 917–30. http://dx.doi.org/10.1108/aa-10-2018-0173.
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Purpose This paper aims to develop a capacitor position in radial distribution networks with a specific end goal to enhance the voltage profile, diminish the genuine power misfortune and accomplish temperate sparing. The issue of the capacitor situation in electric appropriation systems incorporates augmenting vitality and peak power loss by technique for capacitor establishments. Design/methodology/approach This paper proposes a novel strategy using rough thinking to pick reasonable applicant hubs in a dissemination structure for capacitor situation. Voltages and power loss reduction indices of distribution networks hubs are shown by fuzzy enrollment capacities. Findings A fuzzy expert system containing a course of action of heuristic rules is then used to ascertain the capacitor position appropriateness of each hub in the circulation structure. The sizing of capacitor is solved by using hybrid artificial bee colony–cuckoo search optimization. Practical implications Finally, a short-term load forecasting based on artificial neural network is evaluated for predicting the size of the capacitor for future loads. The proposed capacitor allocation is implemented on 69-node radial distribution network as well as 34-node radial distribution network and the results are evaluated. Originality/value Simulation results show that the proposed method has reduced the overall losses of the system compared with existing approaches.
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Kumar, R. Lokesh. "Hybrid Neural Network Methodology to Detect and Predict Seismic Activities." Journal of Soft Computing Paradigm 4, no. 3 (2022): 150–59. http://dx.doi.org/10.36548/jscp.2022.3.004.
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The prediction of earthquakes, which can be devastating calamities, has proven to be a challenging research area. Because it involves filtering data to disturbed day changes, the contribution from multi-route effects and typical day-to-day fluctuations even on quiet days, the extraction of earthquake-induced features from this parameter requires intricate processing. Nevertheless, many researchers have successfully used several seismological concepts for computing the seismic features, employing the maximum Relevance and Minimum Redundancy (mRMR) criteria to extract the relevant features. The Artificial Neural Network (ANN) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are the primary soft computing tools that can be collaborated to detect and estimate earthquakes positively. The model in ANFIS is developed using subtractive clustering and grid partitioning procedures. The outcome shows that compared to ANFIS, ANN is more effective at predicting earthquake magnitude. Furthermore, it has been discovered that using this method to estimate earthquake magnitude is highly quick and cost-effective. Compared to earlier prediction studies, the acquired numerical findings show enhanced prediction performance for all the regions considered.
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Tanadi, Julio, and Benfano Soewito. "Improving Artificial Neural Network Indoor Positioning System Accuracy using Hybrid Method." International Journal of Engineering Trends and Technology 69, no. 11 (2021): 156–60. http://dx.doi.org/10.14445/22315381/ijett-v69i11p220.
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Ilic, Slobodan, Srdjan Vukmirovic, Aleksandar Erdeljan, and Filip Kulic. "Hybrid artificial neural network system for short-term load forecasting." Thermal Science 16, suppl. 1 (2012): 215–24. http://dx.doi.org/10.2298/tsci120130073i.
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This paper presents a novel hybrid method for Short-Term Load Forecasting (STLF). The system comprises of two Artificial Neural Networks (ANN), assembled in a hierarchical order. The first ANN is a Multilayer Perceptron (MLP) which functions as integrated load predictor (ILP) for the forecasting day. The output of the ILP is then fed to another, more complex MLP, which acts as an hourly load predictor (HLP) for a forecasting day. By using a separate ANN that predicts the integral of the load (ILP), additional information is presented to the actual forecasting ANN (HLP), while keeping its input space relatively small. This property enables online training and adaptation, as new data become available, because of the short training time. Different sizes of training sets have been tested, and the optimum of 30 day sliding time-window has been determined. The system has been verified on recorded data from Serbian electrical utility company. The results demonstrate better efficiency of the proposed method in comparison to non-hybrid methods because it produces better forecasts and yields smaller mean average percentage error (MAPE).
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F. Anthon Pangruruk and Simon P. Barus. "Comparison of Prediction of the Number of People Exposed to Covid 19 Using the Lagrange Interpolation Method with the Newton Gregory Maju Polynomial Interpolation Method." Formosa Journal of Applied Sciences 2, no. 6 (2023): 1405–26. http://dx.doi.org/10.55927/fjas.v2i6.4853.
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In March 2020 the World Health Organization stated that the Corona Virus pandemic (Covid-19) was due to its massive spread and hit all countries in the world. Academics and practitioners are called upon to carry out research activities in order to obtain a mathematical model that can be used to predict the number of people exposed to Covid-19 or other diseases. The researchers previously tried research to predict the number of people exposed to Covid-19 from early 2021 using the Monte Karlo method, the Hybrid Nonlinear Regression Logistic– Double Exponential Smoothing method, the Arima method, the BackPropagation and Fuzzy Tsukamoto methods, the K-Nearest method. Neighbors, Time Series Analysis method, Winter Method and Long Short Time Memory (LSTM) Artificial Neural Network method.
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Zhang, Tian-hu, and Xue-yi You. "The use of genetic algorithm and self-updating artificial neural network for the inverse design of cabin environment." Indoor and Built Environment 26, no. 3 (2016): 347–54. http://dx.doi.org/10.1177/1420326x15609772.
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The inverse process of computational fluid dynamics was used to explore the expected indoor environment with the preset objectives. An inverse design method integrating genetic algorithm and self-updating artificial neural network is presented. To reduce the computational cost and eliminate the impact of prediction error of artificial neural network, a self-updating artificial neural network is proposed to realize the self-adaption of computational fluid dynamics database, where all the design objectives of solutions are obtained by computational fluid dynamics instead of artificial neural network. The proposed method was applied to the inverse design of an MD-82 aircraft cabin. The result shows that the performance of artificial neural network is improved with the increase of computational fluid dynamics database. When the number of computational fluid dynamics cases is more than 80, the success rate of artificial neural network increases to more than 40%. Comparing to genetic algorithm and computational fluid dynamics, the proposed hybrid method reduces about 53% of the computational cost. The pseudo solutions are avoided when the self-updating artificial neural network is adopted. In addition, the number of computational fluid dynamics cases is determined automatically, and the requirement of human adjustment is avoided.
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Albasri, Halah, and Sepanta Naimi. "Development of a hybrid artificial neural network method for evaluation of the sustainable construction projects." Acta logistica 10, no. 3 (2023): 345–52. http://dx.doi.org/10.22306/al.v10i3.378.
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Planned methods may be developed to improve the efficiency of building construction. The construction business is profoundly impacted by the prevalence of inaccurate cost and schedule prediction. The main strategy to improve the project performance is to evaluate the hybrid sustainable materials using the artificial neural network (ANN) method based on the effective factors in construction projects in Iraq. This strategy needs an effective method to classify the project input representation and specify the accurate activity of each factor. This paper uses a hybrid artificial neural network to correlate and classify the sustainable hybrid of construction projects to evaluate their performance. The contribution of this method is the selection of the Multi-Criteria Decision-Maker method (MCDM) based on time and cost-effective factors correlated with the artificial neural network method. A dynamic selection procedure for project materials may be created using the existing technique as an evolutionary model for successful project completion. The MCDM observed that the appropriate sustainable material was considered as the main factor with a rank of 0.823 for cost effect and 0.735 for time effect and the main influence factor in Iraqi projects was the building height. The results present superior functional cost evaluation results correlated with the selection of hybrid sustainable materials.
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Alshahrani, Mohammed, Mohammed Al-Jabbar, Ebrahim Mohammed Senan, Ibrahim Abdulrab Ahmed, and Jamil Abdulhamid Mohammed Saif. "Hybrid Methods for Fundus Image Analysis for Diagnosis of Diabetic Retinopathy Development Stages Based on Fusion Features." Diagnostics 13, no. 17 (2023): 2783. http://dx.doi.org/10.3390/diagnostics13172783.
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Diabetic retinopathy (DR) is a complication of diabetes that damages the delicate blood vessels of the retina and leads to blindness. Ophthalmologists rely on diagnosing the retina by imaging the fundus. The process takes a long time and needs skilled doctors to diagnose and determine the stage of DR. Therefore, automatic techniques using artificial intelligence play an important role in analyzing fundus images for the detection of the stages of DR development. However, diagnosis using artificial intelligence techniques is a difficult task and passes through many stages, and the extraction of representative features is important in reaching satisfactory results. Convolutional Neural Network (CNN) models play an important and distinct role in extracting features with high accuracy. In this study, fundus images were used for the detection of the developmental stages of DR by two proposed methods, each with two systems. The first proposed method uses GoogLeNet with SVM and ResNet-18 with SVM. The second method uses Feed-Forward Neural Networks (FFNN) based on the hybrid features extracted by first using GoogLeNet, Fuzzy color histogram (FCH), Gray Level Co-occurrence Matrix (GLCM), and Local Binary Pattern (LBP); followed by ResNet-18, FCH, GLCM and LBP. All the proposed methods obtained superior results. The FFNN network with hybrid features of ResNet-18, FCH, GLCM, and LBP obtained 99.7% accuracy, 99.6% precision, 99.6% sensitivity, 100% specificity, and 99.86% AUC.
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Harandizadeh, Hooman. "Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 34, no. 1 (2020): 114–26. http://dx.doi.org/10.1017/s0890060420000025.
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AbstractThis research intends to investigate a new hybrid artificial intelligence (AI) technique compared to some common CPT methods in estimating axial ultimate pile bearing capacity (UPBC) using cone penetration test (CPT) data in geotechnical engineering applications. A data series of 108 samples was collected in order to develop a new hybrid structure of an adaptive neuro-fuzzy inference system (ANFIS) network, and the group method of the data handling (GMDH) type neural network was optimized by applying the particle swarm optimization (PSO) algorithm over the hybrid ANFIS-GMDH topology, which leads to a new hybrid AI model called as ANFIS-GMDH-PSO. The derived database provides information related to pile load tests, in situ field CPT data, and soil–pile information for introducing the proposed hybrid neural system. The cross-section of the pile toe, average cone tip resistance along embedded pile length, and sleeve frictional resistance along the shaft had been considered as input parameters for the proposed network. The results of this research indicated that the proposed ANFIS-GMDH-PSO model predicted the UPBC with an acceptable precision compared to various CPT methods, including Schmertmann, De Kuiter & Bringen, and LPC/LPCT methods. Moreover, ANFIS-GMDH-PSO network model performance was compared to CPT-based models in terms of statistical criteria in order to achieve a best fitted model. From the statistical results, it was found that the developed ANFIS-GMDH-PSO model has achieved a higher accuracy level in terms of statistical indices compared to CPT-based empirical methods, such as Schmertmann model, De Kuiter & Beringen model, and Bustamante & Gianeselli for predicting driven pile ultimate bearing capacity.