Stability analysis for bidirectional associative memory neural networks: A new global asymptotic approach

This study employs specific and appropriate criteria to investigate the global stability of hybrid bidirectional associative memory (BAM) neural networks with time delays. We establish new and more general conditions for global asymptotic robust stability (GARS) in time-delayed BAM neural networks at the equilibrium point. This represents the primary objective and novelty of this paper. The derived conditions are independent of the system parameter delay in BAM neural networks. Finally, we provide numerical examples to illustrate the applicability and effectiveness of our conclusions with respect to network parameters.