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Dynamic modeling and control of nonlinear processes using neural network techniques

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4 Author(s)
G. Karsai ; Dept. of Electr. Eng., Vanderbilt Univ., Nashville, TN, USA ; K. Andersen ; G. E. Cook ; K. Ramaswamy

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

Published in:

Intelligent Control, 1989. Proceedings., IEEE International Symposium on

Date of Conference:

25-26 Sep 1989