Boosting Wavelet Neural Networks Using Evolutionary Algorithms for Short-Term Wind Speed Time Series Forecasting

• Published in: Advances in Computational Intelligence Vol. 11506; pp. 15 - 26
• Main Author: Wei, Hua-Liang
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Boosting; Data-Driven modelling; Evolutionary algorithms; Forecasting; Neural network; Optimization; Time series; Wavelet; Wind speed
• ISBN: 3030205207; 9783030205201
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-20521-8_2

This paper addresses nonlinear time series modelling and prediction problem using a type of wavelet neural networks. The basic building block of the neural network models is a ridge type function. The training of such a network is a nonlinear optimization problem. Evolutionary algorithms (EAs), including genetic algorithm (GA) and particle swarm optimization (PSO), together with a new gradient-free algorithm (called coordinate dictionary search optimization – CDSO), are used to train network models. An example for real speed wind data modelling and prediction is provided to show the performance of the proposed networks trained by these three optimization algorithms.