High speed learning of neural network using fuzzy logic

Author

Adibi, A. ; Salehi, M. ; Heshmatpanah, J. ; Firoozshahi, A. ; Baniardalani, S.

Author_Institution

Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran, Iran

fYear

1993

fDate

16-18 Aug 1993

Firstpage

117

Abstract

We have proposed a fuzzy method to vary and modify the critical coefficients ETA and ALPHA involved with the feed forward multilayer neural network (FF network) to raise the network learning speed. In fact a fuzzy controller has been designed in this regard in order to accept the absolute and the change of error values as its inputs to determine the proper ETA and ALPHA coefficients. This can be done with the aid of the appropriate rule bases that result from the observation and analysis of neural network behavior during its learning interval

Keywords

feedforward neural nets; fuzzy control; fuzzy logic; learning (artificial intelligence); ALPHA coefficients; ETA coefficients; error values; feedforward multilayer neural network; fuzzy controller; fuzzy logic; high speed learning; learning interval; rule bases; Convergence; Feedforward neural networks; Feeds; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Multi-layer neural network; Neural networks; Time of arrival estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1993., Proceedings of the 36th Midwest Symposium on

Conference_Location

Detroit, MI

Print_ISBN

0-7803-1760-2

Type

conf

DOI

10.1109/MWSCAS.1993.343051

Filename

343051