Pauline Ong, Pauline Ong and Zainuddin, Zarita (2023) An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction. Decision Analytics Journal, 6. pp. 1-12.
![[img]](http://eprints.uthm.edu.my/style/images/fileicons/text.png) |
Text
J15860_28b01933e0ffce86e1f06e1f75cc22f8.pdf Restricted to Registered users only Download (1MB) | Request a copy |
Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Cuckoo search algorithm Function approximation Metaheuristic algorithm Optimization Wavelet neural networks |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Mechanical and Manufacturing Engineering |
| Depositing User: | Mr. Mohamad Zulkhibri Rahmad |
| Date Deposited: | 30 Oct 2023 07:18 |
| Last Modified: | 30 Oct 2023 07:18 |
| URI: | http://eprints.uthm.edu.my/id/eprint/10275 |
Actions (login required)
 |
View Item |