Chaotic synchronisation for coupled neural networks based on T-S fuzzy theory

Abstract

Chaotic synchronisation problems for fuzzy neural networks with hybrid coupling are investigated in this paper. A novel concept that can make use of more relaxed variables by employing the new type of augmented matrices with Kronecker product operation is proposed. The proposed method can handle multitude of Kronecker product operation in Lyapunov-Krasovskii functional, and introduce more arbitrary matrices to reduce the conservatism. Since the expression based on linear matrix inequality is used, the synchronisation criteria can be easily checked in practice. Numerical simulation examples are provided to verify the effectiveness and the applicability of the proposed method.

Keywords:

• neural networks
• hybrid coupling
• Takagi-Sugeno (T-S) fuzzy neural networks
• chaotic synchronisation
• free-weighting matrix

Acknowledgements

This work was supported by the National Natural Science Foundation of China (50977008, 61034005, 61104021), the National High Technology Research and Development Program of China (2009AA04Z127) and National Basic Research Program of China (2009CB320601).

Additional information

Notes on contributors

Dawei Gong

Dawei Gong is currently a lecturer with the University of Electronic Science and Technology of China, Chengdu, China. He received the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2012. His current research interests include neural networks, complex networks, fuzzy modelling and control, optimisation in process industries and intelligent optimisation algorithms.

Jinhai Liu

Jinhai Liu is currently an associate professor in Northeastern University. He received the B.S. degree in automation from Harbin Institute of Technology, Harbin, China, in 2002, M.S. degree in power electronics and power transmission in 2005 and Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2009. Dr. Liu's research interests include data-driven fault diagnosis, neural network and safety technology of long pipeline.

Shuangyu Zhao

Shuangyu Zhao is currently a graduate student and will receive M.S. degree in mechanical engineering from University of Electronic Science and Technology of China in 2014. She received the B.S. degree in electrical engineering and automation from University of Electronic Science and Technology of China, Chengdu, China, in 2011. Her research interest is biomimetic robotics.