The robust dissipativity problem is analysed in this article for the Markovian switching complex-valued neural networks perturbed by stochastic noises, where the transition rates of the Markovian switching are uncertain which comprise two categories: completely unknown or unknown but with known upper/lower bounds. The randomly occurring system uncertainties are governed by certain mutually independent Bernoulli-distributed white sequences, which might reflect more realistic dynamical behaviours of the switching network. Based on the generalised It's formula in complex form as well as certain stochastic analysis methods, several mode-dependent dissipativity/passivity criteria are obtained in terms of complex matrix inequalities. Finally, illustrative examples are provided to demonstrate feasibility of the derived results.

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具有随机发生的不确定性和一般不确定转换率的随机马尔可夫切换 CVNN 的耗散性

本文针对受随机噪声扰动的马尔可夫切换复值神经网络分析了鲁棒耗散问题，其中马尔可夫切换的转换率是不确定的，包括两类：完全未知或未知但具有已知上/下界。随机发生的系统不确定性受某些相互独立的伯努利分布的白序列控制，这可能反映了开关网络更真实的动态行为。基于复数形式的广义It公式，结合一定的随机分析方法，得到了复矩阵不等式的若干模态相关耗散/无源判据。最后，提供了说明性示例以证明导出结果的可行性。