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Finite-Time Stabilization of Memristive Neural Networks with Time Delays

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Abstract

This paper investigates the finite-time stabilization (FTS) problem of memristive neural networks (MNNs) with time-varying delays. First, a novel memristive connection weights model is established on the basis of the circuits of neural networks and the switching characteristics of memristor. Compared with the existing models, the improved model can better reflect the characteristics of the memristor. Then, by framing a novel Lyapunov–Krasovskii functional and designing a delayed-feedback controller, new less conservative sufficient criteria are derived for the FTS of MNNs. Eventually, two examples are provided to demonstrate the effectiveness of the results.

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Acknowledgements

This paper was supported by the National Natural Science Foundation of China under Grants 62076229, 61703377 and 61603358.

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Authors and Affiliations

  1. School of Automation, China University of Geosciences, Wuhan, 430074, China

    Leimin Wang, Jie Wu & Xinmei Wang

  2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, China

    Leimin Wang, Jie Wu & Xinmei Wang

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  1. Leimin Wang
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  2. Jie Wu
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  3. Xinmei Wang
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Correspondence to Leimin Wang.

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Wang, L., Wu, J. & Wang, X. Finite-Time Stabilization of Memristive Neural Networks with Time Delays. Neural Process Lett 53, 299–318 (2021). https://doi.org/10.1007/s11063-020-10390-w

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