Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction
Determining the optimal number of hidden nodes and their proper initial locations are essentially crucial before the wavelet neural networks (WNNs) start their learning process. In this paper, a novel strategy known as the modified cuckoo search algorithm (MCSA), is proposed for WNNs initialization...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2019
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| Online Access: | http://eprints.uthm.edu.my/4606/1/AJ%202019%20%28290%29.pdf |
| _version_ | 1825709875996393472 |
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| author | Ong, Pauline Zainuddin, Zarita |
| author_facet | Ong, Pauline Zainuddin, Zarita |
| author_sort | Ong, Pauline |
| collection | UTHM |
| description | Determining the optimal number of hidden nodes and their proper initial locations are essentially crucial before the wavelet neural networks (WNNs) start their learning process. In this paper, a novel strategy known as the modified cuckoo search algorithm (MCSA), is proposed for WNNs initialization in order to improve its generalization performance. The MCSA begins with an initial population of cuckoo eggs, which represent the translation vectors of the wavelet hidden nodes, and subsequently refines their locations by imitating the breeding mechanism of cuckoos. The resulting solutions from the MCSA are then used as the initial translation vectors for the WNNs. The feasibility of the proposed method is evaluated by forecasting a benchmark chaotic time series, and its superior prediction accuracy compared with that of conventional WNNs demonstrates its potential benefit. |
| first_indexed | 2024-03-05T21:49:02Z |
| format | Article |
| id | uthm.eprints-4606 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:49:02Z |
| publishDate | 2019 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | uthm.eprints-46062021-12-07T08:29:00Z http://eprints.uthm.edu.my/4606/ Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction Ong, Pauline Zainuddin, Zarita QA273-280 Probabilities. Mathematical statistics Determining the optimal number of hidden nodes and their proper initial locations are essentially crucial before the wavelet neural networks (WNNs) start their learning process. In this paper, a novel strategy known as the modified cuckoo search algorithm (MCSA), is proposed for WNNs initialization in order to improve its generalization performance. The MCSA begins with an initial population of cuckoo eggs, which represent the translation vectors of the wavelet hidden nodes, and subsequently refines their locations by imitating the breeding mechanism of cuckoos. The resulting solutions from the MCSA are then used as the initial translation vectors for the WNNs. The feasibility of the proposed method is evaluated by forecasting a benchmark chaotic time series, and its superior prediction accuracy compared with that of conventional WNNs demonstrates its potential benefit. Elsevier 2019 Article PeerReviewed text en http://eprints.uthm.edu.my/4606/1/AJ%202019%20%28290%29.pdf Ong, Pauline and Zainuddin, Zarita (2019) Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction. Applied Soft Computing Journal, 80. pp. 374-386. ISSN 1568-4946 https://doi.org/10.1016/j.asoc.2019.04.016 |
| spellingShingle | QA273-280 Probabilities. Mathematical statistics Ong, Pauline Zainuddin, Zarita Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction |
| title | Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction |
| title_full | Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction |
| title_fullStr | Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction |
| title_full_unstemmed | Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction |
| title_short | Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction |
| title_sort | optimizing wavelet neural networks using modified cuckoo search for multi step ahead chaotic time series prediction |
| topic | QA273-280 Probabilities. Mathematical statistics |
| url | http://eprints.uthm.edu.my/4606/1/AJ%202019%20%28290%29.pdf |
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