Global asymptotic stability of stochastic complex-valued neural networks with probabilistic time-varying delays
Global asymptotic stability of stochastic complex-valued neural networks with probabilistic time-varying delays
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DOI：
10.1016/j.matcom.2019.04.001
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
R. Sriraman;Yang Cao;R. Samidurai
中科院分区：
文献类型：
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作者：
R. Sriraman;Yang Cao;R. Samidurai
研究了一类具有概率时变时滞和随机扰动的随机复值神经网络（SCVNNs）全局渐近稳定性问题。基于Lyapunov-Krasovskii泛函（LKF）方法和数学分析技术，将复值神经网络（cvnn）分离为实部和虚部，导出了时滞相关的稳定性判据。进一步，用简化的线性矩阵不等式（lmi）形式给出了得到的充分条件，并可以用Matlab直接求解。最后，给出了两个仿真实例，验证了所提结果的有效性和优越性。
This paper studies the global asymptotic stability problem for a class of stochastic complex-valued neural networks (SCVNNs) with probabilistic time-varying delays as well as stochastic disturbances. Based on the Lyapunov–Krasovskii functional (LKF) method and mathematical analytic techniques, delay-dependent stability criteria are derived by separating complex-valued neural networks (CVNNs) into real and imaginary parts. Furthermore, the obtained sufficient conditions are presented in terms of simplified linear matrix inequalities (LMIs), which can be straightforwardly solved by Matlab. Finally, two simulation examples are provided to show the effectiveness and advantages of the proposed results.