Study of the control-relevant properties of backpropagation neural network models of nonlinear dynamical systems

Abstract

Neural networks have been used for important applications in chemical engineering. Such applications include: fault detection in process control systems, modeling of nonlinear dynamics, control of nonlinear systems, solution of nonlinear programming problems and others. Most of the articles published on the subject consider neural networks to be “black boxes” and concentrate on the applications. After introducing the basic principles of neural networks, we will concentrate on studying control-relevant properties of neural network models of nonlinear systems. In particular, we study the stability of these models as well as the stability of the model's inverse. This provides a background with which to study the neural network-based control algorithms proposed in the literature.