Neurogenetic characterization of fault tolerant switched reluctance motor drives

Author

Arkadan, A.-R.A. ; Belfore, Lee A., II

Author_Institution

Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI, USA

Volume

34

Issue

5

fYear

1998

fDate

9/1/1998 12:00:00 AM

Firstpage

3612

Lastpage

3615

Abstract

This paper presents the results of a study on the feasibility of using artificial neural networks (ANNs) and genetic algorithms (GAs) to predict the performance characteristics of faulted switched reluctance motor (SRRI) drive systems. In this work, the ANNs are applied for their well known interpolation capabilities for highly nonlinear systems. In addition, the GAs are employed for their ability to search a complex structural and parametric space as necessary to find good ANN solutions. Also, an integrated finite elements/state space modeling approach is used to generate training data sets for the SRM drive system. Furthermore, the results are compared to test data

Keywords

electric machine analysis computing; feedforward neural nets; finite element analysis; genetic algorithms; interpolation; learning (artificial intelligence); reluctance motor drives; state-space methods; ANN; artificial neural networks; fault tolerant switched reluctance motor drives; feedforward neural nets; finite elements modeling; genetic algorithms; highly nonlinear systems; interpolation capabilities; neurogenetic characterization; performance characteristics; state space modeling; training data sets; Artificial neural networks; Fault tolerance; Finite element methods; Genetic algorithms; Interpolation; Nonlinear systems; Reluctance machines; Reluctance motors; State-space methods; Training data;

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/20.717853

Filename

717853