Sampled-data state estimation for Markovian jumping fuzzy cellular neural networks with mode-dependent probabilistic time-varying delays

In this paper, the problem of state estimation for Markovian jumping fuzzy cellular neural networks (FCNNs) using sampled-data with mode-dependent probabilistic time-varying delays is investigated. By developing a delay decomposition approach, the information of the delayed states can be taken into full consideration. By introducing a stochastic variable with a Bernoulli distribution, the information of probability distribution of the time-varying delay is considered and transformed into one with deterministic time-varying delay. The main purpose of this paper is to estimate the neuron states through available output measurements such that the dynamics of the estimation error is globally asymptotically stable in the mean square. Based on the Lyapunov-Krasovskii functional including triple integral terms and decomposed integral intervals, delay-distribution-dependent stability criteria are obtained in terms of linear matrix inequalities (LMIs). Finally two numerical examples are given to illustrate the effectiveness of the proposed theoretical results.