An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction
Although the practicability of using wavelet neural networks (WNNs) in nonlinear function approximation has been addressed extensively, selecting the optimal number of hidden nodes and their appropriate initial locations remains a great challenge for WNNs’ initialization. The cuckoo search algorithm...
保存先:
| 主要な著者: | , |
|---|---|
| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Elsevier
2023
|
| 主題: | |
| オンライン･アクセス: | http://eprints.uthm.edu.my/9148/1/J15860_28b01933e0ffce86e1f06e1f75cc22f8.pdf http://eprints.uthm.edu.my/9148/ https://doi.org/10.1016/j.dajour.2023.100188 |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
| 要約: | Although the practicability of using wavelet neural networks (WNNs) in nonlinear function approximation has been addressed extensively, selecting the optimal number of hidden nodes and their appropriate initial locations remains a great challenge for WNNs’ initialization. The cuckoo search algorithm (CSA) is used in this study for optimizing WNNs. The position of the cuckoo eggs represents the translation of the wavelet hidden nodes, which are optimized based on the egg-laying and breeding strategy of cuckoos. The solutions
from the CSA are assigned as initial translation vectors for the WNNs and subsequently evaluated on a few benchmarking functions and real-world applications. Performance assessment demonstrates its superior approximation capability than the existing methods used for WNNs initialization. |
|---|