Name
Title | ||
|---|---|---|
Stability of stochastic fuzzy BAM neural networks with discrete and distributed time-varying delays |
Abstract | ||
|---|---|---|
Abstract Among the various fuzzy models, the well-known Takagi–Sugeno (T–S) fuzzy model is recognized as a popular and powerful tool in approximating a complex nonlinear system. T–S model provides a fixed structure to some nonlinear systems and facilitates the analysis of the system. This paper deals with the global stability of stochastic bidirectional associative memory (BAM) neural networks with discrete and distributed time-varying delays which are represented by the T–S fuzzy models. The stability conditions are derived using Lyapunov–Krasovskii functional combined with the linear matrix inequality (LMI) techniques. Finally, numerical examples are given to demonstrate the correctness of the theoretical results. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s13042-014-0320-7 | Int. J. Machine Learning & Cybernetics |
Keywords | Field | DocType |
Discrete and distributed time-varying delay,Global stability,Linear matrix inequality,Takagi–Sugeno fuzzy,Stochastic fuzzy BAM neural network | Neuro-fuzzy,Nonlinear system,Bidirectional associative memory,Control theory,Fuzzy logic,Correctness,Stability conditions,Artificial neural network,Mathematics,Linear matrix inequality | Journal |
Volume | Issue | ISSN |
8 | 1 | 1868-808X |
Citations | PageRank | References |
6 | 0.49 | 31 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Syed Ali | 1 | 518 | 39.49 |
| P. Balasubramaniam | 2 | 86 | 5.47 |
| Quanxin Zhu | 3 | 1100 | 67.69 |