基于增强零等式方法的延迟马尔可夫跃迁神经网络可达集估计

S. H. Kim, Y. J. Kim, S. H. Lee, O. M. Kwon
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引用次数: 0

摘要

本文提出了估算具有时变延迟的马尔可夫跃迁神经网络(MJNN)可达集的方法。通过建立改进的 Lyapunov-Krasovskii 函数,可以获得对延迟依赖性具有较小保守性的条件。利用积分不等式来估计 MJNNs 的可达集,从而在时间延迟方面获得更有效、更保守的结果。此外,一些数学技术,如增强零等式方法,改善了结果并消除了自由变量。两个数值示例和数字表明,与之前的研究相比,所提出的方法是有效的,而且提供的结果也不那么保守。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reachable set estimation of delayed Markovian jump neural networks based on an augmented zero equality approach
This article suggests the methods to estimate the reachable set of Markovian jump neural networks (MJNNs) with time‐varying delays. By building up improved Lyapunov–Krasovskii functionals, the conditions that have less conservatism for the delay‐dependent can be obtained. Integral inequalities are employed to estimate the reachable set of MJNNs, resulting in more effective and conservative outcomes regarding time delays. Moreover, some mathematical techniques, the augmented zero equality approach, improve the results and eliminated the free variables. Two numerical examples and figures demonstrated that the proposed method was effective and provided less conservative results than previous research.
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