Box and jenkins nonlinear system modelling using RBF neural networks designed by NSGAII

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dc.contributor.authorLamamra, Kheireddine
dc.contributor.authorBelarbi, Khaled
dc.contributor.authorBoukhtini, Souaad
dc.date.accessioned2022-04-27T03:42:53Z
dc.date.available2022-04-27T03:42:53Z
dc.date.issued2015
dc.description.abstractIn this work, we use radial basis function neural network for modeling nonlinear systems. Generally, the main problem in artificial neural network is often to find a better structure. The choice of the architecture of artificial neural network for a given problem has long been a problem. Developments show that it is often possible to find architecture of artificial neural network that greatly improves the results obtained with conventional methods. We propose in this work a method based on No Sorting Genetic Algorithm II (NSGA II) to determine the best parameters of a radial basis function neural network. The NSGAII should provide the best connection weights between the hidden layer and output layer, find the parameters of the radial function of neurons in the hidden layer and the optimal number of neurons in the hidden layers and thus ensure learning necessary. Two functions are optimized by NSGAII: the number of neurons in the hidden layer of the radial basis function neural network, and the error which is the difference between desired input and the output of the radial basis function neural network. This method is applied to modeling Box and Jenkins system. The obtained results are very satisfactory.ar
dc.identifier.urihttp://hdl.handle.net/123456789/12964
dc.language.isoenar
dc.publisherSpringerar
dc.subjectNSGAIIar
dc.subjectRadial basis function (RBF) neural networksar
dc.subjectOptimizationar
dc.subjectModellingar
dc.subjectNon linear systemar
dc.subjectBox and Jenkins systemar
dc.titleBox and jenkins nonlinear system modelling using RBF neural networks designed by NSGAIIar
dc.typeArticlear
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