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:
Online Access:http://eprints.usm.my/42264/
http://eprints.usm.my/42264/1/ONG_PAULINE.pdf
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author Ong , Pauline
author_facet Ong , Pauline
author_sort Ong , Pauline
building USM Institutional Repository
collection Online Access
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.
first_indexed 2025-11-15T17:48:26Z
format Thesis
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institution Universiti Sains Malaysia
institution_category Local University
language English
last_indexed 2025-11-15T17:48:26Z
publishDate 2011
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spelling usm-422642019-04-12T05:26:40Z 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.
spellingShingle QA1-939 Mathematics
Ong , Pauline
An Improved Wavelet Neural Network For Classification And Function Approximation
title 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_short An Improved Wavelet Neural Network For Classification And Function Approximation
title_sort improved wavelet neural network for classification and function approximation
topic QA1-939 Mathematics
url http://eprints.usm.my/42264/
http://eprints.usm.my/42264/1/ONG_PAULINE.pdf