NDLI: Synchronization of two coupled neurons using CMAC neural networks

Content Provider	IEEE Xplore Digital Library
Author	Cheng-Hung Chuang Yu-Hsiung Lin Chih-Hu Wang Chun-Fei Hsu
Copyright Year	2011
Description	Author affiliation: Department of Electrical Engineering, Chung Hua University, Hsinchu 300, Taiwan, R.O.C. (Cheng-Hung Chuang; Yu-Hsiung Lin; Chih-Hu Wang; Chun-Fei Hsu)
Abstract	Cerebellar model articulation controller (CMAC) neural network has been already validated that it can approximate a nonlinear function over a domain of interest to any desired accuracy. This paper proposes an adaptive PI CMAC neural control (APICNC) system. The proposed APICNC system is composed of a feedback controller, a CMAC neural controller and a compensation controller. The CMAC neural controller is used to mimic system dynamics and the compensation controller is designed to dispel the approximation error. The Lyapunov stability theorem is utilized to derive the parameter learning algorithm, so that the stability of the APICNC system can be guaranteed. Then, the proposed APICNC system is applied to the coupled nonlinear cable model chaotic system. Simulation results verify the proposed APICNC system can achieve a favorable performance and the developed proportional-integral (PI) type learning algorithm can speed up the convergence of the tracking error.
Starting Page	789
Ending Page	794
File Size	722711
Page Count	6
File Format	PDF
ISBN	9781457703058
ISSN	21601348
e-ISBN	9781457703089
DOI	10.1109/ICMLC.2011.6016795
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lead Chaotic communication Synchronization Approximation error Adaptive control Neurons CMAC Lyapunov stability theorem Chaotic system
Content Type	Text
Resource Type	Article

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