| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 388030 | Expert Systems with Applications | 2009 | 6 Pages |
Abstract
In this paper, the Takagi–Sugeno (TS) fuzzy model representation is extended to the stability analysis for uncertain Cohen–Grossberg type bidirectional associative memory (BAM) neural networks with time-varying delays using linear matrix inequality (LMI) theory. A novel LMI-based stability criterion is obtained by using LMI optimization algorithms to guarantee the asymptotic stability of uncertain Cohen–Grossberg BAM neural networks with time varying delays which are represented by TS fuzzy models. Finally, the proposed stability conditions are demonstrated with numerical examples.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
M. Syed Ali, P. Balasubramaniam,