Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction

• Published in: Applied soft computing Vol. 80; pp. 374 - 386
• Main Authors: Ong, Pauline, Zainuddin, Zarita
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-07-2019
• Subjects: Chaotic time series; Cuckoo search algorithm; Metaheuristic algorithm; Translation vector; Wavelet neural networks; Chaotic time series; Cuckoo search algorithm; Translation vector; Metaheuristic algorithm; Wavelet neural networks
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.04.016

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.
•We propose a novel improved cuckoo search algorithm.•Its effectiveness is tested in optimizing wavelet neural networks.•Performance comparison in chaotic time series forecasting is made.•The proposed model shows higher generalization capability than others.