Global Exponential Stability in Hybrid Bi-Directional Associative Memory Neural Networks with Time Delays

During recent decades, a class of neural networks related to bi directional associative memory（BAM） have been proposed These models generalized the single layer autoassociative Hebbian correlator to a two layer pattern matched heteroassociative circuit Therefore, this class of networks possesses good application prospects in the areas of pattern recognition, signal and image processing, etc In this paper, convergence characteristics of hybrid bi directional associative memory neural networks with discrete delays are investigated Halanay type inequalities are employed to obtain delay independent sufficient conditions for the networks to converge exponentially toward the equilibria associated with the constant input sources It is shown that the estimates obtained from the Halanay type inequalities improve the estimates obtained from the Lyapunov methods The results are easily verified and a numerical example is given to illustrate the correctness of the results obtained