| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 4633999 | Applied Mathematics and Computation | 2008 | 7 Pages |
Abstract
In the paper, the global asymptotic stability of equilibrium is considered for continuous bidirectional associative memory (BAM) neural networks of neutral type by using the Lyapunov method. A new stability criterion is derived in terms of linear matrix inequality (LMI) to ascertain the global asymptotic stability of the BAM. The LMI can be solved easily by various convex optimization algorithms. A numerical example is illustrated to verify our result.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Ju H. Park, C.H. Park, O.M. Kwon, S.M. Lee,