Title :
State estimation using artificial neural networks
Author :
Kanekar, Ashish J. ; Feliachi, Ali
Author_Institution :
Dept. of Electr. & Comput. Eng., West Virginia Univ., Morgantown, WV, USA
Abstract :
The state estimation problem is addressed using artificial neural networks. The neural networks used are the adaptive linear combiner and a multilayer net. Training is performed by using several Kalman filter solutions to set the different weights. The derived neural network estimator gives state estimates when the system is subjected to unknown noises. Examples are given to illustrate the proposed approach
Keywords :
neural nets; parallel architectures; state estimation; Kalman filter solutions; adaptive linear combiner; artificial neural networks; multilayer net; state estimation; unknown noises; Artificial neural networks; Automatic logic units; Equations; Hopfield neural networks; Multi-layer neural network; Neural networks; Noise measurement; Q measurement; State estimation; Time measurement;
Conference_Titel :
System Theory, 1990., Twenty-Second Southeastern Symposium on
Conference_Location :
Cookeville, TN
Print_ISBN :
0-8186-2038-2
DOI :
10.1109/SSST.1990.138206
