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Implementation of intelligent controller using neural network state estimator

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3 Author(s)
Bialasiewicz, J.T. ; Dept. of Electr. Eng. & Comput. Sci., Colorado Univ., Denver, CO, USA ; Proano, J.C. ; Wall, E.T.

It is shown that the neural network can be used as a state estimator in a model-reference intelligent control system. Its learning capability and noise rejection characteristic are illustrated by the results of a simulation study. The implementation of the state estimator by a neural network was possible due to the development of a proper structure of the neural network which is capable of simulating the dynamic behavior of a linear or nonlinear system. This capability is achieved by use of a time-dependent learning process

Published in:

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

Date of Conference:

25-26 Sep 1989