Parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks

Evolving Spiking Neural Network (ESNN) is the third generation of artificial neural network that has been widely used in numerous studies in recent years. However, there are issues of ESSN that need to be improved; one of which is its parameters namely the modulation factor (Mod), similarity factor...

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Main Author:	Roslan, Farezdzuan
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/80972/1/FarezdzuanRoslanMFC2018.pdf
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id	my-utm-ep.80972
record_format	uketd_dc
spelling	my-utm-ep.809722019-07-24T00:13:36Z Parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks 2018-08 Roslan, Farezdzuan QA75 Electronic computers. Computer science Evolving Spiking Neural Network (ESNN) is the third generation of artificial neural network that has been widely used in numerous studies in recent years. However, there are issues of ESSN that need to be improved; one of which is its parameters namely the modulation factor (Mod), similarity factor (Sim) and threshold factor (C) that have to be manually tuned for optimal values that are suitable for any particular problem. The objective of the proposed work is to automatically determine the optimum values of the ESNN parameters for various datasets by integrating the Firefly Algorithm (FA) optimizer into the ESNN training phase and adaptively searching for the best parameter values. In this study, FA has been modified and improved, and was applied to improve the accuracy of ESNN structure and rates of classification accuracy. Five benchmark datasets from University of California, Irvine (UCI) Machine Learning Repository, have been used to measure the effectiveness of the integration model. Performance analysis of the proposed work was conducted by calculating classification accuracy, and compared with other parameter optimisation methods. The results from the experimentation have proven that the proposed algorithms have attained the optimal parameters values for ESNN. 2018-08 Thesis http://eprints.utm.my/id/eprint/80972/ http://eprints.utm.my/id/eprint/80972/1/FarezdzuanRoslanMFC2018.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:119782 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 Roslan, Farezdzuan Parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks
description	Evolving Spiking Neural Network (ESNN) is the third generation of artificial neural network that has been widely used in numerous studies in recent years. However, there are issues of ESSN that need to be improved; one of which is its parameters namely the modulation factor (Mod), similarity factor (Sim) and threshold factor (C) that have to be manually tuned for optimal values that are suitable for any particular problem. The objective of the proposed work is to automatically determine the optimum values of the ESNN parameters for various datasets by integrating the Firefly Algorithm (FA) optimizer into the ESNN training phase and adaptively searching for the best parameter values. In this study, FA has been modified and improved, and was applied to improve the accuracy of ESNN structure and rates of classification accuracy. Five benchmark datasets from University of California, Irvine (UCI) Machine Learning Repository, have been used to measure the effectiveness of the integration model. Performance analysis of the proposed work was conducted by calculating classification accuracy, and compared with other parameter optimisation methods. The results from the experimentation have proven that the proposed algorithms have attained the optimal parameters values for ESNN.
format	Thesis
qualification_level	Master's degree
author	Roslan, Farezdzuan
author_facet	Roslan, Farezdzuan
author_sort	Roslan, Farezdzuan
title	Parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks
title_short	Parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks
title_full	Parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks
title_fullStr	Parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks
title_full_unstemmed	Parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks
title_sort	parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2018
url	http://eprints.utm.my/id/eprint/80972/1/FarezdzuanRoslanMFC2018.pdf
_version_	1747818287691988992

Parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks

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