A POD-Based Center Selection for RBF Neural Network in Time Series Prediction Problems

Authors:
Wenbo Zhang;Xinchen Guo;Chaoyong Wang;Chunguo Wu
Affiliations:
College of Computer Science, Jilin Normal University, Siping 136000, China;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Year:
2007

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Abstract

Center selection based on proper orthogonal decomposition (POD) is presented to select centers for the radial basis function (RBF) neural network in prediction of nonlinear time series. The proposed method takes advantages of the time-sequence feature in time series data and enables the center selection to be implemented in a parallel manner. Simulations on a benchmark problem and on two predictions of stock prices show that the presented method can be applied effectively to the prediction of nonlinear time series. Besides possessing higher precisions in training and testing, the proposed method has stronger generalization and noise resistance abilities, compared to several other popular center selection methods.