Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data
Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this paper, in order to improve the predictive capability of WNNs, the types of activation functions used in the hidden layer of the WNN were varied. The modified WNNs were then appl...
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| Language: | en |
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Elsevier
2011
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| Online Access: | http://eprints.uthm.edu.my/4220/1/AJ%202017%20%28582%29.pdf http://eprints.uthm.edu.my/4220/ https://dx.doi.org/10.1016/j.asoc.2011.06.013 |
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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 | Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this paper, in order to improve the predictive capability of WNNs, the types of activation functions used in the hidden layer of the WNN were varied. The modified WNNs were then applied in approximating a benchmark piecewise function. Subsequently, performance comparisons with other developed methods in studying the same benchmark function were made. An assessment analysis showed that this proposed approach outperformed the rest. The efficiency of the modified WNNs was explored through a real-world application problem-specifically, the prediction of time-series pollution data at Texas of United States. The comparative experimental results showed that integrating different wavelet families into the hidden layer of WNNs leads to superior performance |
| format | Article |
| id | my.uthm.eprints-4220 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2011 |
| publisher | Elsevier |
| record_format | eprints |
| spelling | my.uthm.eprints-42202021-12-01T06:12:19Z http://eprints.uthm.edu.my/4220/ Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data Zainuddin, Zarita Pauline, Ong TK7800-8360 Electronics Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this paper, in order to improve the predictive capability of WNNs, the types of activation functions used in the hidden layer of the WNN were varied. The modified WNNs were then applied in approximating a benchmark piecewise function. Subsequently, performance comparisons with other developed methods in studying the same benchmark function were made. An assessment analysis showed that this proposed approach outperformed the rest. The efficiency of the modified WNNs was explored through a real-world application problem-specifically, the prediction of time-series pollution data at Texas of United States. The comparative experimental results showed that integrating different wavelet families into the hidden layer of WNNs leads to superior performance Elsevier 2011 Article PeerReviewed text en http://eprints.uthm.edu.my/4220/1/AJ%202017%20%28582%29.pdf Zainuddin, Zarita and Pauline, Ong (2011) Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data. Applied Soft Computing, 11 (8). pp. 4866-4874. ISSN 1568-4946 https://dx.doi.org/10.1016/j.asoc.2011.06.013 |
| spellingShingle | TK7800-8360 Electronics Zainuddin, Zarita Pauline, Ong Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data |
| title | Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data |
| title_full | Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data |
| title_fullStr | Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data |
| title_full_unstemmed | Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data |
| title_short | Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data |
| title_sort | modified wavelet neural network in function approximation and its application in prediction of time-series pollution data |
| topic | TK7800-8360 Electronics |
| url | http://eprints.uthm.edu.my/4220/1/AJ%202017%20%28582%29.pdf http://eprints.uthm.edu.my/4220/ https://dx.doi.org/10.1016/j.asoc.2011.06.013 |
| url_provider | http://eprints.uthm.edu.my/ |