The impact of VMAX activation function in particle swarm optimization neural network

Back propagation (BP) Network is the most common technique in Artificial Neural Network (ANN) learning. However, major disadvantages of BP are its convergence rate is relatively slow and always being trapped at the local minima. Therefore, latest optimization technique, Particle Swarm Optimization (...

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Main Author:	Lee, Yiew Siang
Format:	Thesis
Language:	English
Published:	2008
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/9456/1/LeeYiewSiangFSKSM2008.pdf
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id	my-utm-ep.9456
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spelling	my-utm-ep.94562018-07-19T01:38:53Z The impact of VMAX activation function in particle swarm optimization neural network 2008-06 Lee, Yiew Siang QA75 Electronic computers. Computer science Back propagation (BP) Network is the most common technique in Artificial Neural Network (ANN) learning. However, major disadvantages of BP are its convergence rate is relatively slow and always being trapped at the local minima. Therefore, latest optimization technique, Particle Swarm Optimization (PSO) is chosen and applied in feed forward neural network to enhance the network learning. In conventional PSO, maximum velocity, Vmax serves as a constraint that controls the maximum global exploration ability PSO can have. By setting a too small maximum velocity, maximum global exploration ability is limited and PSO will always favor a local search no matter what the inertia weight is. By setting a large maximum velocity, PSO can have a large range of exploration ability. Therefore, in this study, different activation functions will apply in the PSO Vmax function in order to control global exploration of particles and increase the convergence rate as well as correct classification. The preliminary results show that Vmax hyperbolic tangent function give promising results in term of convergence rate and classification compared to Vmax sigmoid function and standard Vmax function. 2008-06 Thesis http://eprints.utm.my/id/eprint/9456/ http://eprints.utm.my/id/eprint/9456/1/LeeYiewSiangFSKSM2008.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information Systems
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Lee, Yiew Siang The impact of VMAX activation function in particle swarm optimization neural network
description	Back propagation (BP) Network is the most common technique in Artificial Neural Network (ANN) learning. However, major disadvantages of BP are its convergence rate is relatively slow and always being trapped at the local minima. Therefore, latest optimization technique, Particle Swarm Optimization (PSO) is chosen and applied in feed forward neural network to enhance the network learning. In conventional PSO, maximum velocity, Vmax serves as a constraint that controls the maximum global exploration ability PSO can have. By setting a too small maximum velocity, maximum global exploration ability is limited and PSO will always favor a local search no matter what the inertia weight is. By setting a large maximum velocity, PSO can have a large range of exploration ability. Therefore, in this study, different activation functions will apply in the PSO Vmax function in order to control global exploration of particles and increase the convergence rate as well as correct classification. The preliminary results show that Vmax hyperbolic tangent function give promising results in term of convergence rate and classification compared to Vmax sigmoid function and standard Vmax function.
format	Thesis
qualification_level	Master's degree
author	Lee, Yiew Siang
author_facet	Lee, Yiew Siang
author_sort	Lee, Yiew Siang
title	The impact of VMAX activation function in particle swarm optimization neural network
title_short	The impact of VMAX activation function in particle swarm optimization neural network
title_full	The impact of VMAX activation function in particle swarm optimization neural network
title_fullStr	The impact of VMAX activation function in particle swarm optimization neural network
title_full_unstemmed	The impact of VMAX activation function in particle swarm optimization neural network
title_sort	impact of vmax activation function in particle swarm optimization neural network
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information Systems
publishDate	2008
url	http://eprints.utm.my/id/eprint/9456/1/LeeYiewSiangFSKSM2008.pdf
_version_	1747814734298611712

The impact of VMAX activation function in particle swarm optimization neural network

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