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Robust asymptotic stability for BAM neural networks with time-varying delays via LMI approach |
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| Authors: | Jia Liu Guang-deng Zong Yun-xi Zhang |
| Affiliation: | (1) College of Computer Science and Engineering, Chongqing University, Chongqing, 400044, China;(2) Department of Computer and Modern Education Technology, Chongqing Education College, Chongqing, 400067, China;(3) College of Computer Science, Chongqing Technology and Business University, Chongqing, 400067, China |
| Abstract: | Several novel stability conditions for BAM neural networks with time-varying delays are studied. Based on Lyapunov-Krasovskii functional combined with linear matrix inequality approach, the delay-dependent linear matrix inequality (LMI) conditions are established to guarantee robust asymptotic stability for given delayed BAM neural networks. These criteria can be easily verified by utilizing the recently developed algorithms for solving LMIs. A numerical example is provided to demonstrate the effectiveness and less conservatism of the main results. |
| Keywords: | robust asymptotic stability bidirectional associative memory (BAM) neural networks timevarying delays linear matrix inequality(LMI) Lyapunov-Krasovskii functional |
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