Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Existence and stability of almost periodic solution for BAM neural networks with delays
Applied Mathematics and Computation
Global asymptotic stability of delayed bi-directional associative memory neural networks
Applied Mathematics and Computation
Delay-dependent exponential stability for a class of neural networks with time delays
Journal of Computational and Applied Mathematics
Stability analysis on a neutral neural network model
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Variable-time impulses in BAM neural networks with delays
Neurocomputing
Journal of Control Science and Engineering
| Hi-index | 0.01 |
This paper deals with the problem of the delay-dependent asymptotic stability analysis for bidirectional associative memory (BAM) neural networks of neutral type. Two cases of time delays in which whether the neutral delays are equal to the state delays or not are involved. The activation functions are supposed to be bounded and globally Lipschitz continuous, which are more general than the usual bounded monotonically increasing ones such as the activation functions of the sigmoidal type. By introducing some new integral inequalities and resorting to the Lyapunov-Krasovskii functional approach, one novel delay-dependent condition is established checking the asymptotic stability for a given BAM neural system. All the conditions are presented in terms of linear matrix inequalities (LMIs), which can be easily checked by using recently developed algorithms in solving LMIs. Two numerical examples are provided to show the reduced conservatism of the main results.