Chaos and asymptotical stability in discrete-time recurrent neural networks with generalized input-output function
The conditions for asymptotic stability for the occurrance of bifurcations and finally for the appearance of chaotic behaviour beyond a threshold of \(\Delta t\) are analyzed and founded by means of a number of theorems in the case of discrete time recurrent neural networks with input-output functions in the form of a generalized sigmoid function. Limited numerical applications illustrate the theory.
scientific article; zbMATH DE number 1782078