Exponential stabilization of fuzzy inertial neural networks with mixed delays

Jing Han, Guici Chen, Guodong Zhang
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Abstract

This paper discusses the problem of exponential stabilization for a class of fuzzy inertial neural networks(FINNs) with mixed delays. By using Lyapunov stability theory and some inequality techniques, several new criteria are derived to get global exponential stability of the investigated FINNs. Compared with the previous works on inertial neural networks(INNs) without fuzzy terms or only consider common time delays, our systems considered here are more general and meaningful. Furthermore, we get the exponential stabilization criteria directly from the FINNs themselves without the reduced-order method. At last, illustrative examples are given to show the correctness of the results.
混合时滞模糊惯性神经网络的指数镇定
讨论了一类具有混合时滞的模糊惯性神经网络的指数镇定问题。利用Lyapunov稳定性理论和一些不等式技术,导出了一些新的判别准则来得到所研究的finn的全局指数稳定性。与以往不考虑模糊项或只考虑常见时滞的惯性神经网络相比,本文所考虑的系统更具有通用性和意义。此外,我们不使用降阶方法,直接从finn本身得到指数镇定判据。最后通过算例说明了所得结果的正确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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