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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
http://eprints.usm.my/42264/
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spelling my.usm.eprints.42264 http://eprints.usm.my/42264/ An Improved Wavelet Neural Network For Classification And Function Approximation 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 NonPeerReviewed application/pdf en http://eprints.usm.my/42264/1/ONG_PAULINE.pdf Ong , Pauline (2011) An Improved Wavelet Neural Network For Classification And Function Approximation. PhD thesis, Universiti Sains Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
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
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
publishDate 2011
url http://eprints.usm.my/42264/1/ONG_PAULINE.pdf
http://eprints.usm.my/42264/
_version_ 1643710456565596160
score 13.211869

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