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Modeling a SOFC stack based on GA-RBF neural networks identification

Xiao-Juan Wu, Xin-Jian Zhu, Guang-Yi Cao, Heng-Yong Tu
2007 Journal of Power Sources  
In this paper, a nonlinear offline model of the solid oxide fuel cell (SOFC) is built by using a radial basis function (RBF) neural network based on a genetic algorithm (GA).  ...  During the process of modeling, the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters.  ...  In this work, a RBF neural network based on a genetic algorithm is employed to establish a black-box model for the SOFC.  ... 
doi:10.1016/j.jpowsour.2007.01.086 fatcat:izmu4vqxxjhjhnrwnbbxnm32t4

Energy Consumption Prediction Model of Public Buildings Based on PSO-RBF

Ling Cao, Nian-yan Huang
2017 DEStech Transactions on Computer Science and Engineering  
By analyzing thechange characteristics of energy consumption of public buildings in hot summer and cold winter zone, a building energy consumption prediction model based on RBF neural network is established  ...  On this basis, particle swarm optimization is used to optimize our model, and the building energy consumption prediction model based on PSO-RBF is established.  ...  Common evolutionary algorithm has genetic algorithm, particle swarm optimization algorithm and other algorithms.  ... 
doi:10.12783/dtcse/aics2016/8183 fatcat:77h5qb3yabbink2p7msekuns5i

Hierarchical Radial Basis Function Neural Networks for Classification Problems [chapter]

Yuehui Chen, Lizhi Peng, Ajith Abraham
2006 Lecture Notes in Computer Science  
The purpose of this study is to identify the hierarchical radial basis function neural networks and select important input features for each sub-RBF neural network automatically.  ...  This framework allows input variables selection, over-layer connections for the various nodes involved.  ...  HiRBF cascading together two RBF networks, where both network have different structure but using the same algorithms.  ... 
doi:10.1007/11759966_128 fatcat:yteunspl4bgbfkgqept334xzka

An Improved Quality Evaluation Method for Foreign Trade English Using GA-RBF Neural Network

Yueqing Yang, Ren Ge, Jinxue Huang, Mian Ahmad Jan
2022 Mobile Information Systems  
First, an improved genetic algorithm is utilized to obtain the weight factor of the neural network, which is the data input of the neural network.  ...  Second, the middle layer of the network is optimized, so that the output efficiency can be further improved. Finally, the improved and optimized neural network is simulated.  ...  Genetic Algorithm Optimization Radial Basis Function (RBF) Neural Network RBF Neural Network.  ... 
doi:10.1155/2022/3329908 fatcat:5676eigyxnae7bjkg5nps6hudi

A HYBRID predicting model for the daily Photovoltaic output based on fuzzy clustering of meteorological data and joint algorithm of GAPS and RBF neural network

Wang Jinpeng, Zhou Yang, Guan Xin, Jeremy-Gillbanks, Zhao Xin
2022 IEEE Access  
An algorithm for forecasting the evaluation of the short-term PV output based on fuzzy clustering of meteorological data and a joint algorithm of the Genetic Algorithm Programming System (GAPS) and Radial  ...  Although the Radial Basis Function (RBF) network is already widely utilized in photovoltaic prediction, its prediction error is too large.  ...  algorithm for optimizing the initial weight threshold of the RBF neural network model.  ... 
doi:10.1109/access.2022.3159655 fatcat:t7kp6s3cmjdchgafjcry6tlmr4

Quantitative Modelling in Economics with Advanced Artificial Neural Networks

Lukas Falat, Lucia Pancikova
2015 Procedia Economics and Finance  
Except for the standard RBF, authors also test their own new versions of this neural network combined with other techniques of the ML.  ...  Authors add the evolutionary approach into the ANN and also combine the standard algorithm for adapting weights of the ANN with an unsupervised clustering algorithm called K-means.  ...  Acknowledgements This paper was supported by VEGA grant project 1/0942/14: Dynamic modelling and soft techniques in prediction of economic variables.  ... 
doi:10.1016/s2212-5671(15)01619-6 fatcat:bvltxsarfrcvnhpupzfne63whu

Construction of Tunable Radial Basis Function Networks Using Orthogonal Forward Selection

Sheng Chen, Xia Hong, B.L. Luk, C.J. Harris
2009 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes.  ...  This OFS-LOO algorithm is computationally efficient, and it is capable of constructing parsimonious RBF networks that generalize well.  ...  For the LROLS-LOO algorithm, the single RBF variance was optimized via cross-validation.  ... 
doi:10.1109/tsmcb.2008.2006688 pmid:19095548 fatcat:ym73an4utveujiivc7qvght2xi

Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles

Renqiang Wang, Donglou Li, Keyin Miao
2020 Journal of Marine Science and Engineering  
Then, the improved genetic algorithms (GA) were used to optimize the network parameters online to improve their approximation performance.  ...  In addition, a comparative study with the sliding mode control algorithm based on an RBF network and fuzzy neural network showed that, under the same conditions, the stabilization time of the intelligent  ...  D.L. was responsible for managing and coordinating responsibilities and implementing research activity plans, including literature retrieval and data collection.  ... 
doi:10.3390/jmse8030210 fatcat:krr26znocjgjbn3oq6ykaigjvm

Sum and Product Kernel Regularization Networks [chapter]

Petra Kudová, Terezie Šámalová
2006 Lecture Notes in Computer Science  
As other MASes, Bang consists of environment and agents that communicate via messages. soft computing, neural networks, genetic algorithms we believe in hybrid models, i.e. combination of different approaches  ...  It is a distributed, multiprocess/multi-thread, user-friendly, modular environment allowing for data-driven hybrid modelling with components like artificial neural networks, genetic algorithms, etc.  ...  Definition and properties RKHS is a Hilbert space of functions defined over Ω ⊂ d with the property that for each x ∈ Ω the evaluation functional on H given by F (Aronszajn, 1950) This implies existence  ... 
doi:10.1007/11785231_7 fatcat:ipfcsu5khngs5kwag5rwqkchkm

A novel Hybrid RBF Neural Networks model as a forecaster

Oguz Akbilgic, Hamparsum Bozdogan, M. Erdal Balaban
2013 Statistics and computing  
We develop a new computational procedure using model selection based on information-theoretic principles as the fitness function using the genetic algorithm (GA) to carry out subset selection of best predictors  ...  HRBF-NN is a flexible forecasting technique that integrates regression trees, ridge regression, with radial basis function (RBF) neural networks (NN).  ...  In this study, we use singlepoint crossover to carry out the variable selection via the genetic algorithm.  ... 
doi:10.1007/s11222-013-9375-7 fatcat:2rbjshe5ozgirapchxzw5xhboi

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

Lukas Falat, Dusan Marcek, Maria Durisova
2016 The Scientific World Journal  
The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average.  ...  They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined withK-means clustering  ...  (supplemented by genetic algorithms for weights adaptation) and simple moving average tool for modeling the error part of the RBF.  ... 
doi:10.1155/2016/3460293 pmid:26977450 pmcid:PMC4761754 fatcat:iuq7b6iqnrfgrmpcule7lo7eyu

A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression

Chih-Hung Wu, Gwo-Hshiung Tzeng, Rong-Ho Lin
2009 Expert systems with applications  
A novel hybrid genetic algorithm (HGA) was adapted to search for the optimal type of kernel function and kernel parameter values of SVR to increase the accuracy of SVR.  ...  The proposed model was tested at an electricity load forecasting competition announced on the EUNITE network. The results showed that the new HGA-SVR model outperforms the previous models.  ...  Thus, we believe our proposed HGA-SVR model is able to handle huge data sets and can easily and efficiently be combined with the integer genetic algorithm and real-valued genetic algorithm for developing  ... 
doi:10.1016/j.eswa.2008.06.046 fatcat:ez7ieb6fjfgwpkihlkg3lhndj4


Te-Sheng Li
2006 Journal of the Chinese Institute of Industrial Engineers  
This paper proposes a method of genetic algorithm (GA) based neural network for feature selection that retains sufficient information for classification purposes.  ...  This method combines a genetic algorithm with an artificial neural network classifier, such as back-propagation (BP) neural classifier, radial basis function (RBF) classifier or learning vector quantization  ...  Different neural network models can be employed in the proposed GA-based feature selection approach, such as BP, RBF and LVQ.  ... 
doi:10.1080/10170660609508996 fatcat:zzpumhf6xjcp7eji3tvt57cihe

Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits

Mohsen Yoosefzadeh-Najafabadi, Dan Tulpan, Milad Eskandari
2021 PLoS ONE  
respectively, was the most accurate algorithm and, therefore, selected as the metaClassifier for the E-B algorithm.  ...  Furthermore, for the first time in this study, we allied the E-B with the genetic algorithm (GA) to model the optimum values of yield components in an ideotype genotype in which the yield is maximized.  ...  Robert Brandt for their technical support. We would also like to thank Mrs. Maryam Vazin for her assistance with the field data collection.  ... 
doi:10.1371/journal.pone.0250665 pmid:33930039 pmcid:PMC8087002 fatcat:nuey5wf4xjduzd2wqauue7ov6y

Short-term wind speed forecasting using artificial neural networks for Tehran, Iran

Farivar Fazelpour, Negar Tarashkar, Marc A. Rosen
2016 International Journal of Energy and Environmental Engineering  
Keywords Wind energy Á Wind speed forecasting Á Artificial neural networks with radial basis function Á Adaptive neuro-fuzzy inference system Á Artificial neural network-genetic algorithm Á Artificial  ...  algorithm hybrid and artificial neural networkparticle swarm optimization) are utilized to accurately forecast short-term wind speed data for Tehran, Iran.  ...  Results of artificial neural network-genetic algorithm hybrid model (ANN-GA) The ANN-GA model is trained by a genetic algorithm with Tehran's wind speed data as input data.  ... 
doi:10.1007/s40095-016-0220-6 fatcat:zb2farwobfbmpd4eljitcqt424
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