反应扩散作用下马尔可夫跳变惯性神经网络的无源性分析

Lingyun Sun, Xuelian Wang, Yuqing Qin, Lei Su, Hao Shen, Jing Wang
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引用次数: 0

摘要

研究了具有马尔可夫跳变参数和反应扩散项的惯性神经网络的无源性分析。通过适当的变量变换,将原二阶微分系统转化为一阶微分系统。重点研究了马尔可夫跳跃反应-扩散神经网络的无源性。然后,基于Lyapunov稳定性理论,建立了基于线性矩阵不等式的充分准则,以保证神经网络的理想被动性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Passivity Analysis of Markov Jump Inertial Neural Networks Subject to Reaction-Diffusion
This paper considers the passivity analysis of inertial neural networks with Markov jump parameters and reaction-diffusion terms. The original second-order differential system, by utilizing a suitable variable transformation, is transformed into a first-order one. The focus is on investigating the passive property of Markov jump reaction-diffusion neural networks. Then, based on Lyapunov stability theory, some sufficient criteria in terms of linear matrix inequality are established to guarantee the desired passive performance of neural networks.
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