Toward the use of set-membership identification in efficient training of feedforward neural networks

Author

Deller, J.R., Jr.

Author_Institution

Dept. of Electr. Eng., Michigan State Univ., East Lansing, MI, USA

fYear

1990

fDate

1-3 May 1990

Firstpage

207

Abstract

The application of the theory of set-membership identification to the development of efficient learning algorithms for neural networks is discussed. Some results relevant to the application of the method to nonlinear feedforward networks are presented. The techniques discussed have the potential to significantly improve the efficiency of teaching neural networks by employing novel data selection criteria based on set-theoretic constraints

Keywords

learning systems; neural nets; set theory; data selection criteria; feedforward neural networks; learning algorithms; nonlinear feedforward networks; set-membership identification; set-theoretic constraints; teaching; Control systems; Feedforward neural networks; Intelligent networks; Neural networks; Samarium; Signal processing; Signal processing algorithms; Speech processing; Training data; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1990., IEEE International Symposium on

Conference_Location

New Orleans, LA

Type

conf

DOI

10.1109/ISCAS.1990.111974

Filename

111974

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2616972