A fuzzy modeling approach for the solution of an inverse electrostatic problem

Author

Morabito, F.C. ; Coccorese, E.

Author_Institution

Dept. of Electron. Eng. & Appl. Math., Calabria Univ., Italy

Volume

32

Issue

3

fYear

1996

fDate

5/1/1996 12:00:00 AM

Firstpage

1330

Lastpage

1333

Abstract

A numerical technique based on a suitable combination of artificial neural networks (ANNs) and fuzzy logic (FL) is presented. It is shown how the ANN solution of typical inverse problems can take advantage of the introduction of fuzzy information. The study case is an inverse electrostatic problem of some relevance for nondestructive testing (NDT) applications. The performance of both standard ANNs and the novel hybrid neuro-fuzzy model are compared, and it is shown that the structured approach is superior to the unstructured one, particularly in terms of speed of the learning phase

Keywords

electrical engineering; electrical engineering computing; electrostatics; fuzzy logic; fuzzy neural nets; inverse problems; learning (artificial intelligence); nondestructive testing; ANN; NDT applications; artificial neural networks; fuzzy information; fuzzy logic; fuzzy modeling approach; hybrid neuro-fuzzy model; inverse electrostatic problem; learning phase; nondestructive testing; numerical technique; performance; structured approach; unstructured approach; Artificial neural networks; Data mining; Design optimization; Electrostatics; Fuzzy logic; Inverse problems; Mathematics; Neurons; Nondestructive testing; Testing; Training data;

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/20.497491

Filename

497491