Dissipativity of the stochastic Markovian switching CVNNs with randomly occurring uncertainties and general uncertain transition rates
Qiang Li, Jinling Liang
Abstract
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 Itoˆ'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.