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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | en |
| Published: |
2011
|
| Subjects: | |
| Online Access: | http://eprints.usm.my/42264/1/ONG_PAULINE.pdf http://eprints.usm.my/42264/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1834502531078684672 |
|---|---|
| author | Ong , Pauline |
| author_facet | Ong , Pauline |
| author_sort | Ong , Pauline |
| building | Hamzah Sendut Library |
| collection | Institutional Repository |
| content_provider | Universiti Sains Malaysia |
| content_source | USM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.usm.eprints.42264 |
| institution | Universiti Sains Malaysia |
| language | en |
| publishDate | 2011 |
| record_format | eprints |
| 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. |
| 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/42264/1/ONG_PAULINE.pdf http://eprints.usm.my/42264/ |
| url_provider | http://eprints.usm.my/ |