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具有Markov切换Cohen-Grossberg神经网络的依概率渐近稳定性

Asymptotic Stability with Probability of Markov Switching Cohen-Grossberg Neural Network
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摘要 研究具有Markov切换和非线性扰动的Cohen-Grossberg神经网络的依概率稳定性问题,基于Lyapunov稳定性理论,利用Markov切换转移概率的性质和线性矩阵不等式(LMI)工具,得到系统基于矩阵不等式的依概率渐近稳定性充分条件。 This paper is concerned with the stability in probability of Cohen-Grossberg neural network with Markov switching and nonlinear disturbance.Based on the Lyapunov stability theory,by using the virtue of the properties of the transition probability of Markov switching and the linear matrix inequality techniques,some sufficient conditions for the asymptotic stability in probability of systems are established in terms of matrix inequality.
作者 吴媛媛 余珮琳 孙云霞 程培 WU Yuan-yuan;YU Pei-lin;SUN Yun-xia;CHENG Pei(School of Mathematical Science,Anhui University,Hefei 230601,China)
出处 《佳木斯大学学报(自然科学版)》 CAS 2019年第1期156-158,170,共4页 Journal of Jiamusi University:Natural Science Edition
关键词 COHEN-GROSSBERG神经网络 Markov切换 非线性扰动 依概率渐近稳定 Cohen-Grossberg neural networks Markov switching nonlinear perturbations asymptotic stability in probability
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