Convergence analysis of background neural networks with two subnetworks
The convergence of background neural networks with two identical, unstructured subnetworks that inhibit each other is studied. The convergence properties include global attractivity and complete stability analysis. Furthermore, it shows that shifting the background level affects the existence and stability of the equilibrium point. Adding noise to the networks, it will fluctuate around an equilibrium point.