Using wavelet neural networks for the optimal design of electromagnetic devices

Author

Qing, Wu ; Xueqin, Shen ; Qingxin, Yang ; Weili, Yan

Author_Institution

Hebei Univ. of Technol., Tianjin, China

Volume

33

Issue

2

fYear

1997

fDate

3/1/1997 12:00:00 AM

Firstpage

1928

Lastpage

1930

Abstract

A feedforward neural network based on the wavelet transform which can be applied to the approximation of complex nonlinear functions is discussed. A wavelet neural network can establish an exact model through a self-adaptive procedure by learning input/output maps from the training sets which are generated by finite element analysis. The structure of the network can be definitely developed, and the learning speed is increased. We have applied it to the optimization design of an AC vacuum contactor with a DC exciting electrical circuit and obtained a satisfactory scheme

Keywords

feedforward neural nets; finite element analysis; learning (artificial intelligence); optimisation; power engineering computing; vacuum contactors; wavelet transforms; AC vacuum contactor; DC exciting electrical circuit; complex nonlinear functions; electromagnetic devices; feedforward neural network; finite element analysis; input/output maps learning; learning speed; optimal design; optimization design; self-adaptive procedure; training sets; wavelet neural networks; Bandwidth; Electromagnetic devices; Electromagnetic modeling; Feedforward neural networks; Mathematical model; Neural networks; Signal resolution; Time frequency analysis; Wavelet analysis; Wavelet transforms;

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/20.582669

Filename

582669