Standard representation and stability analysis of dynamic artificial neural networks: A unified approach

Abstract

A framework and stability conditions are presented for the analysis of stability of three different classes of dynamic artificial neural networks: (1) neural state space models, (2) global input-output models, and (3) dynamic recurrent neural networks. The models are transformed into a standard nonlinear operator form for which linear matrix inequality-based stability analysis is applied. Theory and numerical examples are used to draw connections and make comparisons to stability conditions reported in the literature for dynamic artificial neural networks.