An Improved Wavelet Neural Network For Classification And Function Approximation

Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this thesis, two different approaches were proposed for improving the predictive capability of WNNs. First, the types of activation functions used in the hidden layer of the WNN were...

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Main Author:	Ong , Pauline
Format:	Thesis
Language:	English
Published:	2011
Subjects:	QA1-939 Mathematics
Online Access:	http://eprints.usm.my/42264/1/ONG_PAULINE.pdf
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id	my-usm-ep.42264
record_format	uketd_dc
spelling	my-usm-ep.422642019-04-12T05:26:40Z An Improved Wavelet Neural Network For Classification And Function Approximation 2011-01 Ong , Pauline QA1-939 Mathematics Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this thesis, two different approaches were proposed for improving the predictive capability of WNNs. First, the types of activation functions used in the hidden layer of the WNN were varied. Second, the proposed enhanced fuzzy c-means clustering algorithm—specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm—was employed in selecting the locations of the translation vectors of the WNN. The modified WNN was then applied in the areas of classification and function approximation. 2011-01 Thesis http://eprints.usm.my/42264/ http://eprints.usm.my/42264/1/ONG_PAULINE.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Matematik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA1-939 Mathematics
spellingShingle	QA1-939 Mathematics Ong , Pauline An Improved Wavelet Neural Network For Classification And Function Approximation
description	Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this thesis, two different approaches were proposed for improving the predictive capability of WNNs. First, the types of activation functions used in the hidden layer of the WNN were varied. Second, the proposed enhanced fuzzy c-means clustering algorithm—specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm—was employed in selecting the locations of the translation vectors of the WNN. The modified WNN was then applied in the areas of classification and function approximation.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ong , Pauline
author_facet	Ong , Pauline
author_sort	Ong , Pauline
title	An Improved Wavelet Neural Network For Classification And Function Approximation
title_short	An Improved Wavelet Neural Network For Classification And Function Approximation
title_full	An Improved Wavelet Neural Network For Classification And Function Approximation
title_fullStr	An Improved Wavelet Neural Network For Classification And Function Approximation
title_full_unstemmed	An Improved Wavelet Neural Network For Classification And Function Approximation
title_sort	improved wavelet neural network for classification and function approximation
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Matematik
publishDate	2011
url	http://eprints.usm.my/42264/1/ONG_PAULINE.pdf
_version_	1747821047362617344

An Improved Wavelet Neural Network For Classification And Function Approximation

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