Study of predicting combined chaotic time series using neural networks

Study of predicting combined chaotic time series using neural networks

The combined chaotic time series is predicted by using the standard feed-forward neural networks (NN). Henon and Lozi systems are used to generate the combined chaotic time series. From the forecasting results, it can be concluded that the NN, which is trained by improved back-propagation (BP) algor...

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Bibliographic Details
Main Authors: WANG, Zhaoxia, Chen, Z.Q., Yuan, Z.Z., Hao, T.Z., Yang, B.H.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2004
Subjects:
Back-propagation (BP) algorithms
Combined chaotic time series
Feed-forward neural networks
Time series prediction
Numerical Analysis and Scientific Computing
OS and Networks
Online Access:https://ink.library.smu.edu.sg/sis_research/5637
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Institution: Singapore Management University
Language: English
Description
Summary:The combined chaotic time series is predicted by using the standard feed-forward neural networks (NN). Henon and Lozi systems are used to generate the combined chaotic time series. From the forecasting results, it can be concluded that the NN, which is trained by improved back-propagation (BP) algorithms, can be well applicable for combined chaotic time series prediction.

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