NDLI: Simulation and Application Research of Chaotic Time Series Prediction Based on RBF Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Ning Ma Wen-jin Zhang Chen Lu
Copyright Year	2010
Abstract	This paper discusses a method for chaotic time series prediction based on radial basis function (RBF) neural network. The number of input nodes for RBF is determined by embedding dimension based on chaotic phase-space reconstruction. Both Grassberger--Procaccia algorithm and Takens' method are employed to calculate minimal embedding dimension of chaotic time series. Finally, the prediction accuracy was evaluated by Mean Square Error (MSE). The chaotic time series data from Lorenz simulation signal and rolling bearing vibration signal was used to verify the proposed method. It was found from the experimental result that, this method is effective and feasible for the prediction of chaotic time series.
Starting Page	5575
Ending Page	5578
File Size	425095
Page Count	4
File Format	PDF
ISBN	9781424468805
e-ISBN	9781424468812
DOI	10.1109/iCECE.2010.1354
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos Radial basis function_(RBF) neural network Presses Simulation Time series analysis Artificial neural networks Prediction Predictive models Systems engineering and theory Chaotic time series
Content Type	Text
Resource Type	Article

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Simulation and Application Research of Chaotic Time Series Prediction Based on RBF Neural Network