An Improved Wavelet Neural Network For Classification And Function Approximation

Ong , Pauline (2011) An Improved Wavelet Neural Network For Classification And Function Approximation. PhD thesis, Universiti Sains Malaysia.

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Abstract

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.

Item Type: Thesis (PhD)
Subjects: Q Science > QA Mathematics > QA1-939 Mathematics
Divisions: Pusat Pengajian Sains Matematik (School of Mathematical Sciences) > Thesis
Depositing User: ASM Ab Shukor Mustapa
Date Deposited: 03 Oct 2018 07:08
Last Modified: 12 Apr 2019 05:26
URI: http://eprints.usm.my/id/eprint/42264

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