Predicting Chaotic Time Series Using Recurrent Neural Network

Chin. Phys. Lett.

2000, Vol. 17

Issue (2): 88-90 DOI:

Original Articles

Predicting Chaotic Time Series Using Recurrent Neural Network

ZHANG Jia-Shu ;XIAO Xian-Ci

Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054

Cite this article:

ZHANG Jia-Shu, XIAO Xian-Ci 2000 Chin. Phys. Lett. 17 88-90

Download: PDF(233KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract A new proposed method, i.e. the recurrent neural network (RNN), is introduced to predict chaotic time series. The effectiveness of using RNN for making one-step and multi-step predictions is tested based on remarkable few datum points by computer-generated chaotic time series. Numerical results show that the RNN proposed here is a very powerful tool for making prediction of chaotic time series.

Keywords: 05.45.+b 05.45.Tp 07.05.Mh

Published: 01 February 2000

PACS:	05.45.+b
	05.45.Tp	(Time series analysis)
	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)

TRENDMD:

URL:

https://cpl.iphy.ac.cn/ OR https://cpl.iphy.ac.cn/Y2000/V17/I2/088

Service
	E-mail this article
	E-mail Alert
	RSS
Articles by authors
	ZHANG Jia-Shu
	XIAO Xian-Ci

Related articles from Frontiers Journals

[1]	LI Nian-Qiang, PAN Wei, YAN Lian-Shan, LUO Bin, XU Ming-Feng, TANG Yi-Long. Quantifying Information Flow between Two Chaotic Semiconductor Lasers Using Symbolic Transfer Entropy[J]. Chin. Phys. Lett., 2012, 29(3): 88-90
[2]	KONG De-Ren, XIE Hong-Bo . Assessment of Time Series Complexity Using Improved Approximate Entropy**[J]. Chin. Phys. Lett., 2011, 28(9): 88-90
[3]	Junaid Ali Khan, Muhammad Asif Zahoor Raja, Ijaz Mansoor Qureshi . Stochastic Computational Approach for Complex Nonlinear Ordinary Differential Equations*[J]. Chin. Phys. Lett., 2011, 28(2): 88-90
[4]	ZHANG Xiao-Yan, MENG Yao-Yong, , ZHANG Hao, OU Wen-Juan, LIU Song-Hao . Fast Nondestructive Identification of Endothelium Corneum Gigeriae Galli Using Visible/Near-Infrared Spectroscopy**[J]. Chin. Phys. Lett., 2011, 28(12): 88-90
[5]	Junaid Ali Khan, Muhammad Asif Zahoor Raja, Ijaz Mansoor Qureshi . Novel Approach for a van der Pol Oscillator in the Continuous Time Domain[J]. Chin. Phys. Lett., 2011, 28(11): 88-90
[6]	Juan A. Lazzús . Predicting Natural and Chaotic Time Series with a Swarm-Optimized Neural Network**[J]. Chin. Phys. Lett., 2011, 28(11): 88-90
[7]	QU Jing-Yi, WANG Ru-Bin, ZHANG Yuan, DU Ying . A Neurodynamical Model for Selective Visual Attention**[J]. Chin. Phys. Lett., 2011, 28(10): 88-90
[8]	YANG Yue, HU Han-Ping, XIONG Wei, CHEN Jiang-Hang . Network Traffic Anomaly Detection Method Based on a Feature of Catastrophe Theory[J]. Chin. Phys. Lett., 2010, 27(6): 88-90
[9]	LI Jian-Kang, SONG Xiang-Rong, YIN Ke. Discrete Capability of the Lempel-Ziv Complexity Algorithm on a Vibration Sequence[J]. Chin. Phys. Lett., 2010, 27(6): 88-90
[10]	DU Ying, LU Qi-Shao. Noise Effects on Temperature Encoding of Neuronal Spike Trains in a Cold Receptor[J]. Chin. Phys. Lett., 2010, 27(2): 88-90
[11]	ZHONG Qi-Shui, YU Yong-Bin, YU Jue-Bang. Fuzzy Modeling and Impulsive Control of a Memristor-Based Chaotic System[J]. Chin. Phys. Lett., 2010, 27(2): 88-90
[12]	DING Gang, LIN Lin, ZHONG Shi-Sheng. Functional Time Series Prediction Using Process Neural Network[J]. Chin. Phys. Lett., 2009, 26(9): 88-90
[13]	YOOER Chi-Feng, XU Jian-Xue, ZHANG Xin-Hua. New Canards Bursting and Canards Periodic-Chaotic Sequence[J]. Chin. Phys. Lett., 2009, 26(7): 88-90
[14]	LIU Na, GUAN Zhi-Hong. The chaotification of discrete Hopfield neural networks via impulsive control[J]. Chin. Phys. Lett., 2009, 26(7): 88-90
[15]	MIAO Qing-Ying, FANG Jian-An, TANG Yang, DONG Ai-Hua. Increasing-order Projective Synchronization of Chaotic Systems with Time Delay[J]. Chin. Phys. Lett., 2009, 26(5): 88-90

Viewed

Full text

Abstract