| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1889961 | Chaos, Solitons & Fractals | 2009 | 9 Pages |
Abstract
In this paper, the Takagi–Sugeno (TS) fuzzy model representation is extended to the stability analysis for uncertain 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 LMI optimization algorithms to guarantee the exponential stability of uncertain 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
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Statistical and Nonlinear Physics
Authors
M. Syed Ali, P. Balasubramaniam,