title: Mode-dependent stochastic stability criteria of fuzzy Markovian jumping neural networks with mixed delays.
abstract: This paper investigates the stochastic stability of fuzzy Markovian jumping neural networks with mixed delays in mean square. The mixed delays include time-varying delay and continuously distributed delay. By using the Lyapunov functional method, Jensen integral inequality, the generalized Jensen integral inequality, linear convex combination technique and the free-weight matrix method, several novel sufficient conditions are derived to ensure the global asymptotic stability of the equilibrium point of the considered networks in mean square. The proposed results, which do not require the differentiability of the activation functions, can be easily checked via Matlab software. Finally, two numerical examples are given to demonstrate the effectiveness and less conservativeness of our theoretical results over existing literature.
authors.affiliation: School of Science, Dalian Jiaotong University, Dalian 116028, PR China. Electronic address: 15566913851@163.com.
authors.forename: Cheng-De
authors.initials: CD
authors.lastname: Zheng
authors.affiliation: School of Science, Dalian Jiaotong University, Dalian 116028, PR China.
authors.forename: Xiaoyu
authors.initials: X
authors.lastname: Zhang
authors.affiliation: School of Information Science and Engineering, Northeastern University, Shenyang 110004, PR China. Electronic address: zhanshan_wang@163.com.
authors.forename: Zhanshan
authors.initials: Z
authors.lastname: Wang
mesh_terms: D003198:Computer Simulation; D017143:Fuzzy Logic; D008390:Markov Chains; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D013997:Time Factors
keywords: Fuzzy neural networks; Jensen integral inequality; Linear convex combination technique; Markovian jumping