A delay-partitioning projection approach to stability analysis of stochastic Markovian jump neural networks with randomly occurred nonlinearities
This paper considers the problem of mean square asymptotic stability of stochastic Markovian jump neural networks with randomly occurred nonlinearities. In terms of linear matrix inequality (LMI) approach and delay-partitioning projection technique, delay-dependent stability criteria are derived for the considered neural networks for cases with or without the information of the delay rates via new LyapunovKrasovskii functionals. We also establish that the conservatism of the conditions is a non-increasing function of the number of delay partitions. An example with simulation results is given to illustrate the effectiveness of the proposed approach.
delay-partitioning projection mean square asymptotic stability time-varying delay randomly occurred nonlinearities (rons) stochastic neural networks