System Identification with Orthogonal Basis Functions and Neural Networks

Abstract

For the control of a process, usually the relation between past input-output data of the process and future outputs must be identified. For the identification of nonlinear systems, neural networks can be used [3]. In this context, neural networks are nonlinear black-box models, to be used with convential parameter estimation methods. Two important models are:

• NNFIR-models: Neural Network Finite Impulse Response models, which use only past process inputs u(k − n) as inputs for the network;

• NNARX-models: Neural Network Auto Regressive with eXogeneous input models, which use past process inputs u(k − n) and past process outputs y(k − n) as inputs for the network.

Submitted to 3rd SNN Neural Network Symposium, September 14–15, 1995 Nijmegen, The Netherlands