Exploring the Impact of Delay on Hopf Bifurcation of a Type of BAM Neural Network Models Concerning Three Nonidentical Delays

In this research, a kind of BAM neural networks containing three nonidentical time delays are explored. Exploiting fixed point knowledge, we examine that the solution to the concerned BAM neural network models exists and is unique. Exploiting a apposite function, we check that the solution to the concerned BAM neural network models is bounded. In line with different delay cases, we systematically analyze the characteristic equations of the concerned BAM neural network models. A set of innovative bifurcation criteria of the concerned BAM neural network models under the six delay situations are acquired. The impact of delay is adequately revealed under different delay cases. The research indicates that delay plays a pivotal role in dominating stability domain and the time that Hopf bifurcation arises. of the concerned BAM neural networks. In order to sustain the theoretical assertions, we present the corresponding software simulation plots. The acquired conclusion of this research are completely novel and has momentous theoretical values in dominating and devising networks.