Stability analysis of a class of discrete-time recurrent neural networks: An LMI approach
Mei Liu · Journal of Zhejiang University(Engineering Science) · 2003
Stability analysis of discretetime recurrent neural networks is seldom researched at present. By using the state space extension method, discretetime recurrent neural networks with sectortype monotone nonlinear activation functions, also known as recurrent multilayer perceptrons (RMLPs), were converted to the forms represented as linear differential inclusions(LDIs). Stability conditions of LDIs were transformed into some linear matrix inequalities (LMIs) which were then solved by MATLAB/LMI TOOLBOX to determine if RMLPs are Lyapunov stable or not. The approach proposed can also be applied to other forms of recurrent neural networks (RNNs).