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...

Full description

Saved in:

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
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Singapore Management University
Language:	English

id	sg-smu-ink.sis_research-6640
record_format	dspace
spelling	sg-smu-ink.sis_research-66402021-01-07T13:12:02Z Study of predicting combined chaotic time series using neural networks WANG, Zhaoxia Chen, Z.Q. Yuan, Z.Z. Hao, T.Z. Yang, B.H. 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. 2004-10-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/5637 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Back-propagation (BP) algorithms Combined chaotic time series Feed-forward neural networks Time series prediction Numerical Analysis and Scientific Computing OS and Networks
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Back-propagation (BP) algorithms Combined chaotic time series Feed-forward neural networks Time series prediction Numerical Analysis and Scientific Computing OS and Networks
spellingShingle	Back-propagation (BP) algorithms Combined chaotic time series Feed-forward neural networks Time series prediction Numerical Analysis and Scientific Computing OS and Networks WANG, Zhaoxia Chen, Z.Q. Yuan, Z.Z. Hao, T.Z. Yang, B.H. Study of predicting combined chaotic time series using neural networks
description	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.
format	text
author	WANG, Zhaoxia Chen, Z.Q. Yuan, Z.Z. Hao, T.Z. Yang, B.H.
author_facet	WANG, Zhaoxia Chen, Z.Q. Yuan, Z.Z. Hao, T.Z. Yang, B.H.
author_sort	WANG, Zhaoxia
title	Study of predicting combined chaotic time series using neural networks
title_short	Study of predicting combined chaotic time series using neural networks
title_full	Study of predicting combined chaotic time series using neural networks
title_fullStr	Study of predicting combined chaotic time series using neural networks
title_full_unstemmed	Study of predicting combined chaotic time series using neural networks
title_sort	study of predicting combined chaotic time series using neural networks
publisher	Institutional Knowledge at Singapore Management University
publishDate	2004
url	https://ink.library.smu.edu.sg/sis_research/5637
_version_	1770575537411981312

Study of predicting combined chaotic time series using neural networks

Similar Items