Prescribed convergence analysis of recurrent neural networks with parameter variations

Abstract

Recurrent neural networks are designed to be convergent to the desired equilibrium point for their applications. Network parameter variations lead network states to other different points. So this paper discusses the prescribed convergence problem of recurrent neural networks with parameter variations. Firstly, we recurrent neural networks’ equilibrium point variation principles when parameters are changed. Then we design one track controller to make recurrent neural networks be convergent to the prescribed equilibrium for known parameter variations. Next, we present one adaptive controller to lead network states to the desired equilibrium for unknown parameter variations. At last, two examples are given for validating the presented methods.