Prediction of Chaotic Time Series Based on Neural Network with Legendre Polynomials

被引：0
|
作者：
Wang, Hongwei [1 ]
Gu, Hong [1 ]
机构：
[1] Dalian Univ Technol, Sch Elect & Informat Engn, Dalian, Liaoning, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2009, PT 1, PROCEEDINGS | 2009年 / 5551卷
关键词：
Neural network; Legendre orthogonal polynomials; Kalman filtering; Singular value decomposition; Chaotic time series;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：836 / 843
页数：8
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