Robust dissipativity and passivity of stochastic Markovian switching
CVNNs with probabilistic time-varying delay and partly unknown
transition rates
Abstract
This paper is devoted to the robust dissipativity and passivity problems
for Markovian switching complex-valued neural networks with
probabilistic time-varying delay, where the transition rates are partly
unknown, which might reflect more realistic dynamical behaviors of the
switching networks. The probabilistic delay is described by a sequence
of bernoulli distributed random variables, and mode-dependent parameter
uncertainties are assumed to be norm-bounded. Based on the complex
version of the generalized It$\hat{o}$’s formula, the
robust analysis tools and the stochastic analysis methods, some
sufficient mode/delay-dependent criteria on the
$(M,N,W)$-dissipativity and passivity are derived in terms of complex
matrix inequalities. In the end of paper, two numerical examples are
presented to illustrate the effectiveness and feasibility of the
obtained results.