DocumentCode
2243852
Title
Chaos prediction and inverse system control based on fuzzy-neural network
Author
Haipeng, Ren ; Ding, Liu
Author_Institution
Xi´´an Univ. of Technol., China
Volume
4
fYear
2001
fDate
2001
Firstpage
148
Abstract
The Sugeno fuzzy-neuron network (FNN) is employed to establish the inverse system model of chaotic systems, and the inverse system method is used to control chaos. The characteristics of this method is learning the motion principles of the chaotic system by FNN and controlling chaos effectively with learned principles instead of establishing the exact analytical model of the chaotic system. Moreover, this method does not require the control objective to be a stationary point or a periodic trajectory. Theoretical analysis and simulations with Logistic and Henon mappings prove that this method is effective
Keywords
Henon mapping; chaos; discrete systems; fuzzy neural nets; learning (artificial intelligence); neurocontrollers; nonlinear dynamical systems; Henon mapping; Logistic mapping; Sugeno fuzzy neuron network; chaos prediction; fuzzy-neural network; inverse system control; inverse system model; motion principles; Analytical models; Biological control systems; Chaos; Control system synthesis; Control systems; Inverse problems; Motion analysis; Nonlinear dynamical systems; Postal services; Predictive models;
fLanguage
English
Publisher
ieee
Conference_Titel
Info-tech and Info-net, 2001. Proceedings. ICII 2001 - Beijing. 2001 International Conferences on
Conference_Location
Beijing
Print_ISBN
0-7803-7010-4
Type
conf
DOI
10.1109/ICII.2001.983797
Filename
983797
Link To Document