Self-organizing adaptive fuzzy neural control for the synchronization of uncertain chaotic systems with random-varying parameters |
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| Authors: | Da Lin Xingyuan Wang |
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| Affiliation: | 1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China;2. School of Automatic and Electronic Information, Sichuan University of Science and Engineering, Zigong 643000, China;1. Department of Mathematics, Shanghai University, 99 Shangda Road, Shanghai 200444, China;2. Department of Basic Mathematics, China University of Petroleum, Qingdao 266580, China;1. School of Computer and Electronics Information, Guangxi University, Nanning 530004, China;2. College of Electrical Engineering, Guangxi University, Nanning 530004, China;1. Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, Cyberjaya, 63100 Selangor, Malaysia;2. Faculty of Engineering, Multimedia University, Jalan Multimedia, Cyberjaya, 63100 Selangor, Malaysia;1. Institute of High Performance Computing, 1 Fusionopolis Way, Singapore 138632, Singapore;2. Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore;1. Key Laboratory of Universal Wireless Communications (Ministry of Education), Beijing University of Posts and Telecommunications, Beijing 100876, China;2. School of Computing and Communications, Lancaster University, LA1 4WA, UK;1. Departamento de Física, Universidade Federal do Paraná, C.P. 19044, 81531-980 Curitiba-PR, Brazil;2. Instituto Federal de Educação, Ciência e Tecnologia de Goiás, C.P. 74130-012, Goiânia, Goiás, Brazil;3. Instituto de Física, Universidade de São Paulo, C. P. 66318, 05315-970 São Paulo, São Paulo, Brazil |
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| Abstract: | This paper proposes a self-organizing adaptive fuzzy neural control (SAFNC) for the synchronization of uncertain chaotic systems with random-varying parameters. The proposed SAFNC system is composed of a computation controller and a robust controller. The computation controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principle controller. The SOFNN identifier is used to online estimate the compound uncertainties with the structure and parameter learning phases of fuzzy neural network (FNN), simultaneously. The structure-learning phase consists of the growing of membership functions, the splitting of fuzzy rules and the pruning of fuzzy rules, and thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. The robust controller is used to attenuate the effects of the approximation error so that the synchronization of chaotic systems is achieved.All the parameter learning algorithms are derived based on the Lyapunov stability theorem to ensure network convergence as well as stable synchronization performance. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper. |
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