NDLI: Multiwavelet networks for prediction of chaotic time series

Content Provider	IEEE Xplore Digital Library
Author	Xieping Gao Fen Xiao
Copyright Year	2004
Description	Author affiliation: Inf. Eng. Coll., Xiangtan Univ., Hunan, China (Xieping Gao; Fen Xiao)
Abstract	Chaotic time series prediction is a very important problem in many applications. The novel idea in this paper is to use principal components analysis (PCA) in conjunction with a novel wavelet neural network, multiwavelet neural network to successfully implement the prediction of chaotic time series. It is shown that the proposed method in this paper has two-fold contributions: (1) the multiwavelet network can essentially avoid the problem of poor convergence and undesired local minimum. (2) PCA can overcome the shortage that all the techniques developed for determining the embedding dimensions are inconvenient to be applied to small sample time series. The experiments also show that the proposed technique in this paper, multiwavelet network with PCA, is a more powerful tool for predicting chaotic series than other prediction techniques.
Starting Page	3328
Ending Page	3332
File Size	304541
Page Count	5
File Format	PDF
ISBN	0780385667
ISSN	1062922X
DOI	10.1109/ICSMC.2004.1400855
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-10-10
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos Neural networks Principal component analysis Feedforward neural networks Multi-layer neural network Multiresolution analysis Wavelet analysis Educational institutions Convergence Signal processing algorithms
Content Type	Text
Resource Type	Article

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