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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2011
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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 |