Matrix measure strategies for stabilization and synchronization of delayed BAM neural networks
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In this paper, global exponential stabilization and synchronization of a class of bidirectional associative memory (BAM) neural networks with time delays are investigated. Based on the Lyapunov stability theory and matrix measure, we present several sufficient conditions for the global exponential stability of the equilibrium point and several criteria for the global exponentially synchronization. The presented results, which are easy to verify and simple to implement in practice, also provide new insights into the exponential stabilization and synchronization of BAM neural networks. One numerical example is given to illustrate the effectiveness of our theoretical results.
KeywordsGlobal exponential stabilization Global exponential synchronization BAM neural networks Matrix measure Time delays
This research is supported by the Natural Science Foundation of China (No. 61374078), Chongqing Research Program of Basic Research and Frontier Technology (No. cstc2015jcyjBX0052), and NPRP Grant # NPRP 4-1162-1-181 from the Qatar National Research Fund (a member of Qatar Foundation).
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