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 :
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