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Finite-Time Anti-synchronization Control of Memristive Neural Networks With Stochastic Perturbations

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Abstract

In this paper, finite-time anti-synchronization control of memristive neural networks with stochastic perturbations is studied. We investigate a class of memristive neural networks with two different types of memductance functions. The purpose of the addressed problem is to design a nonlinear controller which can obtain anti-synchronization of the drive system and the response system in finite time. Based on two kinds of memductance functions, finite-time stability criteria are obtained for memristive neural networks with stochastic perturbations. The analysis in this paper employs differential inclusions theory, finite-time stability theorem, linear matrix inequalities and Lyapunov functional method. These theoretical analysis can characterize fundamental electrical properties of memristive systems and provide convenience for applications in pattern recognition, associative memories, associative learning, etc.. Finally, two numerical examples are given to show the effectiveness of our results.

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Acknowledgments

This paper is supported by the National Natural Science Foundation of China (Grant Nos. 61100204, 61170 269, 61121061), the China Postdoctoral Science Foundation Funded Project (Grant No. 2013M540070), the Beijing Higher Education Young Elite Teacher Project (Grant No. YETP0449), the Beijing Natural Science Foundation (Grant No. 4142016).

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Authors and Affiliations

  1. School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Weiping Wang & Jinghua Xiao

  2. Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Lixiang Li, Haipeng Peng & Yixian Yang

  3. Potsdam Institute for Climate Impact Research, 14473, Potsdam, Germany

    Jürgen Kurths

  4. National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yixian Yang

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  1. Weiping Wang
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  2. Lixiang Li
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  3. Haipeng Peng
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  4. Jürgen Kurths
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  5. Jinghua Xiao
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  6. Yixian Yang
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Correspondence to Lixiang Li.

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Wang, W., Li, L., Peng, H. et al. Finite-Time Anti-synchronization Control of Memristive Neural Networks With Stochastic Perturbations . Neural Process Lett 43, 49–63 (2016). https://doi.org/10.1007/s11063-014-9401-6

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