Global Robust Stability Analysis for Hybrid BAM Neural Networks
N. Mohamed Thoiyab, P. Muruganantham, Nallappan Gunasekaran · 2021
In this paper, we study some new sufficient criteria on global stability analysis for the hybrid bidirectional associative memory (BAM) neural networks with multiple time delays. The ultimate focus of this paper is to derive some new generalized sufficient criteria for the global asymptotic robust stability (GARS) of equilibrium point of the time-delayed BAM neural networks. The obtained sufficient conditions are always independent on the delay of system parameters of hybrid BAM neural networks. Finally, numerical example has been given to explain the effectiveness of our results in terms of network parameters.