Input-to-state stability analysis for memristive BAM neural networks with variable time delays |
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| Authors: | Yong Zhao Jürgen Kurths Lixia Duan |
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| Institution: | 1. School of Mathematics and Information Science, Henan Polytechnical University, Jiaozuo, 454000, China;2. Institute of Physics, Humboldt University, Berlin, 12489, Germany;3. College of Science, North China University of Technology, Beijing, 100144, China;4. Potsdam Institute for Climate Impact Research, Potsdam, 14473, Germany |
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| Abstract: | In this paper, we are concerned with input-to-state stability of a class of memristive bidirectional associative memory (BAM) neural networks with variable time delays. Based on a nonsmooth analysis and set-valued maps, some novel sufficient conditions are obtained for the input-to-state stability of such networks, which extended some known results as particular cases. Finally, a numerical example is presented to illustrate the feasibility and effectiveness of our results. |
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| Keywords: | Input-to-state stability Memristive neural networks Nonsmooth analysis Variable time delays Lyapunov method |
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