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

fYear

1990

fDate

11-13 Mar 1990

Firstpage

552

Lastpage

556

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;

fLanguage

English

Publisher

ieee

Conference_Titel

System Theory, 1990., Twenty-Second Southeastern Symposium on

Conference_Location

Cookeville, TN

ISSN

0094-2898

Print_ISBN

0-8186-2038-2

Type

conf

DOI

10.1109/SSST.1990.138206

Filename

138206

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2338010