Dynamic modeling and control of nonlinear processes using neural network techniques

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.<
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