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Finite-time synchronization of stochastic memristor-based delayed neural networks

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Abstract

The finite-time synchronization problem of stochastic memristor-based delayed neural network is studied. Certain sufficient conditions are got to assure finite-time synchronization of the chaotic stochastic memristor-based neural networks by using differential inclusions theory, finite-time stability theorem, Lyapunov functional, inequality techniques, stochastic analysis theory and designing a suitable controller. Comparison with previous results, the model of memristor-based neural network of this paper is general, and the given stability conditions are novel. Therefore, the obtained results generalize and improve some existing achievements about the memristor-based neural network. Moreover, a numerical simulation example demonstrates the usefulness of the theoretical results.

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Authors and Affiliations

  1. School of Sciences, Southwest Petroleum University, Chengdu, 610500, China

    Yanchao Shi

  2. School of Mathematical Sciences, University of Electronic, Science and Technology of China, Chengdu, 611731, China

    Peiyong Zhu

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  1. Yanchao Shi
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  2. Peiyong Zhu
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Correspondence to Yanchao Shi.

Additional information

Foundation item: Supported by Key Program of Sichuan Provincial Department of Education (16ZA0066), Young scholars development fund of SWPU(201599010003).

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Shi, Y., Zhu, P. Finite-time synchronization of stochastic memristor-based delayed neural networks. Neural Comput & Applic 29, 293–301 (2018). https://doi.org/10.1007/s00521-016-2546-7

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  • DOI: https://doi.org/10.1007/s00521-016-2546-7

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