Dynamic modeling and control of nonlinear processes using neural network techniques

7 January 2003

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

No. 21589860,p. 280-286
https://doi.org/10.1109/isic.1989.238681

Abstract

An adaptive network architecture of nonlinear elements and delay lines is proposed, which can be taught to model the time responses of a nonlinear, multivariable system. The structure has been applied to the modeling and control of a highly coupled multivariable process, namely, gas tungsten arc (GTA) welding. The authors present the architecture, learning algorithm, and experiments which showed the feasibility of the approach, and propose a controller architecture that can regulate a nonlinear, multivariable plant such as GTA welding.

Keywords

PROCESS CONTROL
NEURAL NETWORKS
ARTIFICIAL NEURAL NETWORKS
COMPUTER NETWORKS
ADAPTIVE SYSTEMS
SIGNAL PROCESSING
COMPUTER ARCHITECTURE
DELAY LINES
MIMO
TUNGSTEN

This publication has 1 reference indexed in Scilit:

A multilayered neural network controller
IEEE Control Systems Magazine, 1988