Node decoupled extended Kalman filter based learning algorithm for neural networks

Author

Murtuza, Syed ; Chorian, S.F.

Author_Institution

Sch. of Eng., Michigan Univ., Dearborn, MI, USA

fYear

1994

fDate

16-18 Aug 1994

Firstpage

364

Lastpage

369

Abstract

The use of extended Kalman filter (EKF) is common in estimation of nonlinear system parameters. It has also found application in training of feedforward neural networks. A heuristic modification of the EKF algorithm known as the node decoupled EKF (NDEKF) algorithm, which improves upon the EKF algorithm by significantly reducing computation time and memory requirements, appears very promising. The purpose of this paper is to present the NDEKF algorithm in a form suitable for coding readily into a computer program. Matlab implementation of the algorithm with simulation examples is included

Keywords

Kalman filters; feedforward neural nets; filtering theory; heuristic programming; learning (artificial intelligence); Matlab implementation; feedforward neural networks; heuristic modification; learning algorithm; node decoupled extended Kalman filter; nonlinear system parameters; Artificial neural networks; Computational modeling; Computer architecture; Computer languages; Computer networks; Feedforward neural networks; Laboratories; Manufacturing systems; Neural networks; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control, 1994., Proceedings of the 1994 IEEE International Symposium on

Conference_Location

Columbus, OH

ISSN

2158-9860

Print_ISBN

0-7803-1990-7

Type

conf

DOI

10.1109/ISIC.1994.367790

Filename

367790