Title :
Implementation of intelligent controller using neural network state estimator
Author :
Bialasiewicz, J.T. ; Proano, J.C. ; Wall, E.T.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Colorado Univ., Denver, CO, USA
Abstract :
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
Keywords :
learning systems; model reference adaptive control systems; neural nets; state estimation; dynamic behavior; intelligent controller; learning capability; model-reference; neural network state estimator; noise rejection characteristic; simulation study; Adaptive control; Equations; Intelligent control; Intelligent networks; Multi-layer neural network; Neural networks; Neurofeedback; Neurons; State estimation; Vectors;
Conference_Titel :
Intelligent Control, 1989. Proceedings., IEEE International Symposium on
Conference_Location :
Albany, NY
Print_ISBN :
0-8186-1987-2
DOI :
10.1109/ISIC.1989.238665
