Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation
Specifying the number and locations of the translation vectors for wavelet neural networks (WNNs) is of paramount significance as the quality of approximation may be drastically reduced if initialization of WNNs parameters was not done judiciously. In this paper, an enhanced fuzzy C-means algorithm,...
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| Format: | Article |
| Language: | en |
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Springer Verlag
2013
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| Online Access: | http://eprints.uthm.edu.my/4209/1/AJ%202017%20%28580%29.pdf http://eprints.uthm.edu.my/4209/ https://dx.doi.org/10.1007/s00521-013-1350-x |
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| author | Zainuddin, Zarita Pauline, Ong |
| author_facet | Zainuddin, Zarita Pauline, Ong |
| author_sort | Zainuddin, Zarita |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Specifying the number and locations of the translation vectors for wavelet neural networks (WNNs) is of paramount significance as the quality of approximation may be drastically reduced if initialization of WNNs parameters was not done judiciously. In this paper, an enhanced fuzzy C-means algorithm, specifically the modified point symmetry–based fuzzy C-means algorithm (MPSDFCM), was proposed, in order to determine the optimal initial locations for the translation vectors. The proposed neural network models were then employed in approximating five different nonlinear continuous functions. Assessment analysis showed that integration of the MPSDFCM in the learning phase of WNNs would lead to a significant improvement in WNNs prediction accuracy. Performance comparison with the approaches reported in the literature in approximating the same benchmark piecewise function verified the superiority of the proposed strategy. |
| format | Article |
| id | my.uthm.eprints-4209 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2013 |
| publisher | Springer Verlag |
| record_format | eprints |
| spelling | my.uthm.eprints-42092021-11-28T08:23:12Z http://eprints.uthm.edu.my/4209/ Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation Zainuddin, Zarita Pauline, Ong QA299.6-433 Analysis Specifying the number and locations of the translation vectors for wavelet neural networks (WNNs) is of paramount significance as the quality of approximation may be drastically reduced if initialization of WNNs parameters was not done judiciously. In this paper, an enhanced fuzzy C-means algorithm, specifically the modified point symmetry–based fuzzy C-means algorithm (MPSDFCM), was proposed, in order to determine the optimal initial locations for the translation vectors. The proposed neural network models were then employed in approximating five different nonlinear continuous functions. Assessment analysis showed that integration of the MPSDFCM in the learning phase of WNNs would lead to a significant improvement in WNNs prediction accuracy. Performance comparison with the approaches reported in the literature in approximating the same benchmark piecewise function verified the superiority of the proposed strategy. Springer Verlag 2013 Article PeerReviewed text en http://eprints.uthm.edu.my/4209/1/AJ%202017%20%28580%29.pdf Zainuddin, Zarita and Pauline, Ong (2013) Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation. Neural Computing and Applications, 23 (NIL). pp. 247-259. ISSN 0941-0643 https://dx.doi.org/10.1007/s00521-013-1350-x |
| spellingShingle | QA299.6-433 Analysis Zainuddin, Zarita Pauline, Ong Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation |
| title | Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation |
| title_full | Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation |
| title_fullStr | Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation |
| title_full_unstemmed | Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation |
| title_short | Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation |
| title_sort | design of wavelet neural networks based on symmetry fuzzy c-means for function approximation |
| topic | QA299.6-433 Analysis |
| url | http://eprints.uthm.edu.my/4209/1/AJ%202017%20%28580%29.pdf http://eprints.uthm.edu.my/4209/ https://dx.doi.org/10.1007/s00521-013-1350-x |
| url_provider | http://eprints.uthm.edu.my/ |