Abstract
This paper investigates the problem of stability for a class of discrete-time stochastic neural networks (DSNNs) with mode-dependent delay and Markovian jumping parameters. Throughout this paper, we assume that stochastic disturbances are described by the Brownian motion, jumping parameters are generated from discrete-time discrete-state homogeneous Markov process, and mode-dependent delay d ( r ( k ) ) satisfies d m ≤ d ( r ( k ) ) ≤ d M . By a novel Lyapunov–Krasovskii functional combining with the delay partitioning technique and the free-weighting matrix method in terms of linear matrix inequalities (LMIs), the new stability criterion proves to be less conservative. Finally, numerical examples are given to illustrate the effectiveness of the proposed method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
