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Prediction of chaotic time series based on neural network with legendre polynomials

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Indexed by:期刊论文

Date of Publication:2009-05-26

Journal:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Included Journals:EI、CPCI-S、Scopus

Volume:5551 LNCS

Issue:PART 1

Page Number:836-843

ISSN No.:3642015069

Key Words:Neural network; Legendre orthogonal polynomials; Kalman filtering; Singular value decomposition; Chaotic time series

Abstract:In this paper, a modeling method based on the orthogonal function neural network is proposed. Legendre orthogonal polynomials are selected as the basic functions of the neural network. Kalman filtering algorithm with singular value decomposition is used to confirm the parameters of orthogonal function neural network in order to avoid error delivery and error accumulation. To demonstrate the performance of this modeling method, the simulation on Mackey-Glass chaotic time series is performed. The results show that this method provides effective and accurate prediction. © 2009 Springer Berlin Heidelberg.

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