Synchronization control for arrays of coupled discrete-time delayed Cohen-Grossberg neural networks

Authors:
Tao Li;Aiguo Song;Shumin Fei
Affiliations:
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China;School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China;Key Laboratory of Measurement and Control of CSE, School of Automation, Southeast University, Ministry of Education, Nanjing 210096, China
Venue:
Neurocomputing
Year:
2010

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Abstract

This paper investigates the global exponential synchronization for an array of coupled discrete-time Cohen-Grossberg neural networks (CGNNs) with time-varying delay, in which both the constant coupling and delayed one are considered. Through constructing an improved Lyapunov-Krasovskii functional, the delay-dependent sufficient condition is obtained to guarantee the global synchronization based on linear matrix inequality (LMI) approach. The criterion is presented in terms of LMIs and its feasibility can be easily checked by resorting to Matlab LMI Toolbox. Moreover, the addressed system can include some famous neural network models as its special cases, which can help extend those present results. Finally, the effectiveness of the proposed method can be further illustrated with the help of two numerical examples.