Multistability of memristive neural networks with time‐varying delays |
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| Authors: | Ailong Wu Zhang Jin‐E |
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| Affiliation: | 1. College of Mathematics and Statistics, Hubei Normal University, Huangshi, China;2. Institute for Information and System Science, Xi'an Jiaotong University, Xi'an, China;3. School of Automation, Huazhong University of Science and Technology, Wuhan, China |
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| Abstract: | The recent discovery of memristive neurodynamic systems holds great promise for realizing large‐scale nanoionic circuits. Development of pattern memory analysis for memristive neurodynamic systems poses several challenges. In this article, it shows that an n‐dimensional memristive neural networks with time‐varying delays can have 2n locally exponentially stable equilibria in the saturation region. In addition, local exponential stability of delayed memristive neural networks in any designated region is also characterized, which allows the locally exponentially stable equilibria to locate in the designated region. All of these criteria are very easy to be verified. Finally, the effectiveness of the results are illustrated by two numerical examples. © 2014 Wiley Periodicals, Inc. Complexity 21: 177–186, 2015 |
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| Keywords: | memristive neural networks hybrid systems switched network cluster multistability |
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