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Delay-dependent criteria for periodicity and exponential stability of inertial neural networks with time-varying delays

机译:逾期延迟惯性神经网络的周期性和指数稳定性的延迟相关标准

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This paper mainly studies the periodicity and exponential stability for a class of inertial neural networks (INNs) with time-varying delays. Without utilizing standard reduced-order transformation, by using the continuation theorem and Cauchy-Schwarz inequality, delay-dependent criteria shown by some alge-braic inequalities are derived to ensure the existence of periodic solutions. Furthermore, by means of the fundamental inequality and constructing a modified delay-dependent Lyapunov functional, global exponential stability analysis is obtained based on the derived delay-dependent criteria. In comparison with the reduced order approach applied to the INNs and delay-independent criteria provided for the INNs in the existed literatures, the results obtained in this paper are new. Finally, numerical simulations are carried out to verify the main results. (c) 2020 Elsevier B.V. All rights reserved.
机译:本文主要研究了一类惯性神经网络(旅店)的周期性和指数稳定性,具有时变延迟。在不利用标准阶数转换的情况下,通过使用延续定理和Cauchy-Schwarz不等式,推导出一些Alge-BraiC不等式所显示的延迟依赖标准,以确保周期性解决方案的存在。此外,通过基本的不平等和构建修改的延迟依赖性Lyapunov功能,基于导出的延迟相关标准获得全局指数稳定性分析。与应用于存在的文献中提供的旅馆提供的租赁和延迟独立标准的减少的阶方法相比,本文获得的结果是新的。最后,执行数值模拟以验证主要结果。 (c)2020 Elsevier B.v.保留所有权利。

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