Learning enhancement of radial basis function neural network with harmony search algorithm

Training Radial Basis Function (RBF) neural network with Particle Swarm Optimization (PSO) was considered as a major breakthrough, that overcome the stuck to the local minimum of Back Propagation (BP) and time consuming and computation expensive problems of Genetic Algorithm (GA). However, PSO prove...

Full description

Saved in:

Bibliographic Details
Main Author:	Ahmed, Mohamed Hassan
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/36531/5/MohamedHassanAhmedMFSKSM2013.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.36531
record_format	uketd_dc
spelling	my-utm-ep.365312017-07-05T04:10:42Z Learning enhancement of radial basis function neural network with harmony search algorithm 2013-07 Ahmed, Mohamed Hassan QA75 Electronic computers. Computer science Training Radial Basis Function (RBF) neural network with Particle Swarm Optimization (PSO) was considered as a major breakthrough, that overcome the stuck to the local minimum of Back Propagation (BP) and time consuming and computation expensive problems of Genetic Algorithm (GA). However, PSO proved some problems to achieve the goal, i.e., it converged too fast so that it stuck to the local optimum. Furthermore, particles may move to an invisible region. Therefore, to realize the enhancement of the learning process of RBF and overcome these PSO problems, Harmony Search Meta-Heuristic Algorithm (HSA) was employed to optimize the RBF network and attain the desired objectives. The study conducted a comparative experiments between the integrated HSA-RBF network and the PSORBF network. The results proved that HSA increased the learning capability of RBF neural network in terms of accuracy and correct classification percentage, error convergence rate, and less time consumption with less mean squared error (MSE). The new HSA-RBF model provided higher performance in most cases and promising results with better classification proficiency compared with that of PSO-RBF network. 2013-07 Thesis http://eprints.utm.my/id/eprint/36531/ http://eprints.utm.my/id/eprint/36531/5/MohamedHassanAhmedMFSKSM2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69961?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Ahmed, Mohamed Hassan Learning enhancement of radial basis function neural network with harmony search algorithm
description	Training Radial Basis Function (RBF) neural network with Particle Swarm Optimization (PSO) was considered as a major breakthrough, that overcome the stuck to the local minimum of Back Propagation (BP) and time consuming and computation expensive problems of Genetic Algorithm (GA). However, PSO proved some problems to achieve the goal, i.e., it converged too fast so that it stuck to the local optimum. Furthermore, particles may move to an invisible region. Therefore, to realize the enhancement of the learning process of RBF and overcome these PSO problems, Harmony Search Meta-Heuristic Algorithm (HSA) was employed to optimize the RBF network and attain the desired objectives. The study conducted a comparative experiments between the integrated HSA-RBF network and the PSORBF network. The results proved that HSA increased the learning capability of RBF neural network in terms of accuracy and correct classification percentage, error convergence rate, and less time consumption with less mean squared error (MSE). The new HSA-RBF model provided higher performance in most cases and promising results with better classification proficiency compared with that of PSO-RBF network.
format	Thesis
qualification_level	Master's degree
author	Ahmed, Mohamed Hassan
author_facet	Ahmed, Mohamed Hassan
author_sort	Ahmed, Mohamed Hassan
title	Learning enhancement of radial basis function neural network with harmony search algorithm
title_short	Learning enhancement of radial basis function neural network with harmony search algorithm
title_full	Learning enhancement of radial basis function neural network with harmony search algorithm
title_fullStr	Learning enhancement of radial basis function neural network with harmony search algorithm
title_full_unstemmed	Learning enhancement of radial basis function neural network with harmony search algorithm
title_sort	learning enhancement of radial basis function neural network with harmony search algorithm
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/36531/5/MohamedHassanAhmedMFSKSM2013.pdf
_version_	1747816427455250432

Learning enhancement of radial basis function neural network with harmony search algorithm

Similar Items