Improving learning enhancement radial basis function neural network using improved harmony search algoritim

Radial Basis Function (RBF) neural network training with Particle Swarm Optimization (PSO) overcomes the trapping to the local minimum by Back Propagation (BP) algorithm and slow computation of Genetic Algorithm (GA). However, PSO converged too fast which makes it to be trapped in the local optimum....

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Main Author:	Ahmed Salad, Abdirahman
Format:	Thesis
Published:	2014
Subjects:	QA76 Computer software
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id	my-utm-ep.48479
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spelling	my-utm-ep.484792017-07-26T04:33:33Z Improving learning enhancement radial basis function neural network using improved harmony search algoritim 2014 Ahmed Salad, Abdirahman QA76 Computer software Radial Basis Function (RBF) neural network training with Particle Swarm Optimization (PSO) overcomes the trapping to the local minimum by Back Propagation (BP) algorithm and slow computation of Genetic Algorithm (GA). However, PSO converged too fast which makes it to be trapped in the local optimum. Furthermore, particles may move to an invisible region. Therefore, to enhance the learning process of RBF and overcome the problem associated with PSO, Improved Harmony Search Algorithm (IHSA) was employed to optimize the RBF network to enhance its learning capacity. The study conducted performs comparative analysis between the hybrid of IHSA and RBF network and the PSO-RBF network. The results obtained show that IHSA has increased the learning capability of RBF neural network in terms of correct classification percentage and error convergence rate. The proposed IHSA-RBF model gives higher performance with promising results compared to PSO-RBF network 2014 Thesis http://eprints.utm.my/id/eprint/48479/ masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
topic	QA76 Computer software
spellingShingle	QA76 Computer software Ahmed Salad, Abdirahman Improving learning enhancement radial basis function neural network using improved harmony search algoritim
description	Radial Basis Function (RBF) neural network training with Particle Swarm Optimization (PSO) overcomes the trapping to the local minimum by Back Propagation (BP) algorithm and slow computation of Genetic Algorithm (GA). However, PSO converged too fast which makes it to be trapped in the local optimum. Furthermore, particles may move to an invisible region. Therefore, to enhance the learning process of RBF and overcome the problem associated with PSO, Improved Harmony Search Algorithm (IHSA) was employed to optimize the RBF network to enhance its learning capacity. The study conducted performs comparative analysis between the hybrid of IHSA and RBF network and the PSO-RBF network. The results obtained show that IHSA has increased the learning capability of RBF neural network in terms of correct classification percentage and error convergence rate. The proposed IHSA-RBF model gives higher performance with promising results compared to PSO-RBF network
format	Thesis
qualification_level	Master's degree
author	Ahmed Salad, Abdirahman
author_facet	Ahmed Salad, Abdirahman
author_sort	Ahmed Salad, Abdirahman
title	Improving learning enhancement radial basis function neural network using improved harmony search algoritim
title_short	Improving learning enhancement radial basis function neural network using improved harmony search algoritim
title_full	Improving learning enhancement radial basis function neural network using improved harmony search algoritim
title_fullStr	Improving learning enhancement radial basis function neural network using improved harmony search algoritim
title_full_unstemmed	Improving learning enhancement radial basis function neural network using improved harmony search algoritim
title_sort	improving learning enhancement radial basis function neural network using improved harmony search algoritim
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
_version_	1747817400375443456

Improving learning enhancement radial basis function neural network using improved harmony search algoritim

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