Abstract
Neutral Hopfield neural networks have widely used in optimization problems while its stability analysis has received a great deal of attention. This paper investigates the problem of the global asymptotic stability of stochastic neutral Hopfield neural networks with multiple time-varying delays. Different form the previous reported results, the neural networks are affected by not only stochastic perturbations, but also the time delays including discrete, distributed and neutral types which are a variety of functions related to neural nodes. By constructing a novel Lyapunov functional and using stochastic analysis techniques, we derive the sufficient criteria of the global asymptotic stability of the networks. Finally, some numerical simulations are given to verify the effectiveness of the theoretical results.
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The authors really appreciate the valuable comments of the editors and reviewers. This work was supported by Social science and technology development project in Dongguan under grant 2020507151806 and Natural Science Foundation of Shenzhen under Grant 20200821143547001.
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Li, Y., Xu, S., Feng, J. (2022). Stability Analysis of Stochastic Neutral Hopfield Neural Networks with Multiple Time-Varying Delays. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13345. Springer, Cham. https://doi.org/10.1007/978-3-031-09726-3_12
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DOI: https://doi.org/10.1007/978-3-031-09726-3_12
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