Standard Neural Network Model for the Feedback Stabilization of Intelligent Systems

Abstract

A novel neural network model termed standard neural network model (SNNM) is advanced. Based on the stability analysis of the SNNM, state-feedback control law is then designed for the SNNM to stabilize the closed-loop system. The control design equation is shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signal. Most recurrent neural network (RNNs) and nonlinear systems modelled by neural networks or Takagi and Sugeno fuzzy models can be transformed into the SNNMs to be stability analyzed or stabilization controller synthesized in a unified SNNM's framework. Finally, some examples are presented to illustrate the wide application of the SNNMs to the feedback stabilization of nonlinear systems.