Further mean-square asymptotic stability of impulsive discrete-time stochastic BAM neural networks with Markovian jumping and multiple time-varying delays |
| |
| Authors: | C Sowmiya R Raja Quanxin Zhu G Rajchakit |
| |
| Institution: | 1. Department of Mathematics, Alagappa University, Karaikudi 630004, India;2. Ramanujan Centre for Higher Mathematics, Alagappa University, Karaikudi 630004, India;3. MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, 410081, Hunan, China;4. Department of Mathematics, Faculty of Science, Maejo University, Chiang Mai, Thailand |
| |
| Abstract: | In this paper, the asymptotic stability analysis is investigated for a kind of discrete-time bidirectional associative memory (BAM) neural networks with the existence of perturbations namely, stochastic, Markovian jumping and impulses. Based on the theory of stability, a novel Lyapunov–Krasovskii function is constructed and by utilizing the concept of delay partitioning approach, a new linear-matrix-inequality (LMI) based criterion for the stability of such a system is proposed. Furthermore, the derived sufficient conditions are expressed in the structure of LMI, which can be easily verified by a known software package that guarantees the globally asymptotic stability of the equilibrium point. Eventually, a numerical example with simulation is given to demonstrate the effectiveness and applicability of the proposed method. |
| |
| Keywords: | Corresponding author |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
