A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-12-06
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
A self-constructing fuzzy neural network (SCFNN) has been successfully used for chaotic time series prediction in the literature.
In this paper, we propose the strategy of adding a recurrent path in each node of the hidden layer of SCFNN, resulting in a self-constructing recurrent fuzzy neural network (SCRFNN).
This novel network does not increase complexity in fuzzy inference or learning process.
Specifically, the structure learning is based on partition of the input space, and the parameter learning is based on the supervised gradient descent method using a delta adaptation law.
This novel network can also be applied for chaotic time series prediction including Logistic and Henon time series.
More significantly, it features rapider convergence and higher prediction accuracy.
American Psychological Association (APA)
Li, Qinghai& Lin, Rui-Chang. 2016. A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1112038
Modern Language Association (MLA)
Li, Qinghai& Lin, Rui-Chang. A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network. Mathematical Problems in Engineering No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1112038
American Medical Association (AMA)
Li, Qinghai& Lin, Rui-Chang. A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1112038
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112038