Name
Title | ||
|---|---|---|
Further mean-square asymptotic stability of impulsive discrete-time stochastic BAM neural networks with Markovian jumping and multiple time-varying delays. |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.jfranklin.2018.09.037 | Journal of the Franklin Institute |
Field | DocType | Volume |
Bidirectional associative memory,Control theory,Equilibrium point,Software,Exponential stability,Markovian jumping,Discrete time and continuous time,Artificial neural network,Perturbation (astronomy),Mathematics | Journal | 356 |
Issue | ISSN | Citations |
1 | 0016-0032 | 1 |
PageRank | References | Authors |
0.35 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| C. Sowmiya | 1 | 5 | 1.07 |
| R. Raja | 2 | 180 | 12.58 |
| Quanxin Zhu | 3 | 1100 | 67.69 |
| Grienggrai Rajchakit | 4 | 100 | 11.87 |