Exponential synchronization of Markovian jumping neural networks with partly unknown transition probabilities via stochastic sampled-data control

Exponential synchronization of Markovian jumping neural networks with partly unknown transition probabilities via stochastic sampled-data control, Neurocomputing, This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Abstract: This paper investigates the exponential synchronization for a class of delayed neu-ral networks with Markovian jumping parameters and time varying delays. The considered transition probabilities are assumed to be partially unknown. In addition, the sampling period is assumed to be time-varying that switches between two different values in a random way with given probability. Several delay-dependent synchronization criteria have been derived to guarantee the exponential stability of the error systems and the master systems are stochasti-cally synchronized with the slave systems. By introducing an improved Lyapunov-Krasovskii functional (LKF) including new triple integral terms, free-weighting matrices, partly unknown transition probabilities and combining both the convex combination technique and reciprocal convex technique, a delay-dependent exponential stability criteria is obtained in terms of linear matrix inequalities (LMIs). The information about the lower bound of the discrete time-varying delay is fully used in the LKF. Furthermore, the desired sampled-data synchronization controllers can be solved in terms of the solution to LMIs. Finally, numerical examples are provided to demonstrate the feasibility of the proposed estimation schemes from its gain matrices.