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An improved delay-partitioning approach to stability criteria for generalized neural networks with interval time-varying delays

机译:间隔时变延迟的广义神经网络稳定标准的改进延迟分区方法

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摘要

This paper deals with the problem of stability analysis for generalized delayed neural networks with interval time-varying delays based on the delay-partitioning approach. By constructing a suitable Lyapunov-Krasovskii functional with triple- and four-integral terms and using Jensen's inequality, Wirtinger-based single- and double-integral inequality technique and linear matrix inequalities (LMIs), which guarantees asymptotic stability of addressed neural networks. This LMI can be easily solved via convex optimization algorithm. The novelty of this paper is that the consideration of a new integral inequalities and Lyapunov-Krasovskii functional is shown to be less conservatism, and it takes fully the relationship between the terms in the Leibniz-Newton formula within the framework of LMIs. Moreover, it is assumed that the lower bound of the time-varying delay is not restricted to be zero. Finally, several interesting numerical examples are given to demonstrate the effectiveness and less conservativeness of our theoretical results over well-known examples existing in recent literature.
机译:本文涉及基于延迟分区方法的间隔时变延迟的广义延迟神经网络稳定性分析问题。通过构建具有三级和四分之一术语的合适的Lyapunov-Krasovskii功能,并使用Jensen的不等式,基于Wirtinger的单一和双积分不等式技术和线性矩阵不等式(LMI),可确保寻址神经网络的渐近稳定性。可以通过凸优化算法容易地解决该LMI。本文的新颖性是考虑新的整体不平等和Lyapunov-Krasovskii功能的职能被认为是较少的保守主义,并且在LMIS框架内的Leibniz-Newton公式中的术语之间存在完全的关系。此外,假设时变延迟的下限不限于零。最后,给出了几个有趣的数值例子,以证明我们在最近文献中存在的众所周知的实例的理论结果的有效性和更少的保守性。

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