title: Stochastic synchronization of Markovian jump neural networks with time-varying delay using sampled data.
abstract: In this paper, the problem of sampled-data synchronization for Markovian jump neural networks with time-varying delay and variable samplings is considered. In the framework of the input delay approach and the linear matrix inequality technique, two delay-dependent criteria are derived to ensure the stochastic stability of the error systems, and thus, the master systems stochastically synchronize with the slave systems. The desired mode-independent controller is designed, which depends upon the maximum sampling interval. The effectiveness and potential of the obtained results is verified by two simulation examples.
authors.forename: Zheng-Guang
authors.initials: ZG
authors.lastname: Wu
authors.forename: Peng
authors.initials: P
authors.lastname: Shi
authors.forename: Hongye
authors.initials: H
authors.lastname: Su
authors.forename: Jian
authors.initials: J
authors.lastname: Chu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008390:Markov Chains; D015233:Models, Statistical; D016571:Neural Networks, Computer; D018401:Sample Size; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes