A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series prediction

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• 作者：Min Gan^a ; Hui Peng^b ; ^{huipeng@mail.csu.edu.cn} ; Xue-ping Dong^a
• 关键词：Radial basis function network ; Hybrid training approach ; Time series modeling
• 刊名：Applied Mathematical Modelling
• 出版年：2012
• 期刊代码：6_0307904x
• 类别：cp
• 出版时间：July, 2012
• 卷：36
• 期：7
• 页码：2911-2919
• 文件大小：386 K

摘要

This paper proposes a novel hybrid algorithm for automatic selection of the proper input variables, the number of hidden nodes of the radial basis function (RBF) network, and optimizing network parameters (weights, centers and widths) simultaneously. In the proposed algorithm, the inputs and the number of hidden nodes of the RBF network are represented by binary-coded strings and evolved by a genetic algorithm (GA). Simultaneously, for each chromosome with fixed inputs and number of hidden nodes, the corresponding parameters of the network are real-coded and optimized by a gradient-based fast-converging parameter estimation method. Performance of the presented hybrid approach is evaluated by several benchmark time series modeling and prediction problems. Experimental results show that the proposed approach produces parsimonious RBF networks, and obtains better modeling accuracy than some other algorithms.