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Relaxed exponential passivity criteria for memristor-based neural networks with leakage and time-varying delays

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Abstract

This paper investigates the problem of exponential passivity analysis for memristive neural networks with leakage and time-varying delays. Given that the input and output of the considered neural networks satisfy a prescribed passivity-inequality constraint, the more relaxed criteria are established in terms of linear matrix inequalities by employing nonsmooth analysis and Lyapunov method. The relaxations lie in three aspects: first, this obtained criteria do not really require all the symmetric matrices involved in the employed quadratic Lyapunov-Krasovskii functional to be positive definite; second, the activation functions become general; third, the time-varying delay is not needed to be differentiable. Finally, two numerical examples are given to show the effectiveness of the proposed criteria.

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Acknowledgments

The authors thank the Action Editor and referees for his/her valuable suggestions to improve the article.

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Correspondence to Jianying Xiao.

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This work was supported by National Natural Science Foundation of China (61202045).

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Xiao, J., Zhong, S., Li, Y. et al. Relaxed exponential passivity criteria for memristor-based neural networks with leakage and time-varying delays. Int. J. Mach. Learn. & Cyber. 8, 1875–1886 (2017). https://doi.org/10.1007/s13042-016-0565-4

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  • DOI: https://doi.org/10.1007/s13042-016-0565-4

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