A neural network based fuzzy set model for organizational decision making

Author

Wang, Shouhong ; Archer, Norman P.

Author_Institution

Fac. of Bus., New Brunswick Univ., St. John, NB, Canada

Volume

28

Issue

2

fYear

1998

fDate

5/1/1998 12:00:00 AM

Firstpage

194

Lastpage

203

Abstract

A neural network based fuzzy set model is proposed to support organizational decision making under uncertainty. This model incorporates three theories and methodologies: classical decision-making theory under conflict, as suggested by Luce and Raiffa (1957), the fuzzy set theory of Zadeh (1965, 1984), and a modified version of the backpropagation (BP) neural network algorithm originated by Rumelhart et al. (1986). An algorithm that implements the model is described, and an application of the model to a real data example is used to demonstrate its use

Keywords

backpropagation; decision support systems; decision theory; expert systems; fuzzy set theory; neural nets; uncertainty handling; backpropagation neural network algorithm; classical decision making theory; conflict; fuzzy set theory; neural network based fuzzy set model; organizational decision making; uncertainty; Backpropagation algorithms; Decision making; Fuzzy neural networks; Fuzzy reasoning; Fuzzy set theory; Fuzzy sets; Fuzzy systems; Humans; Neural networks; Uncertainty;

fLanguage

English

Journal_Title

Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on

Publisher

ieee

ISSN

1094-6977

Type

jour

DOI

10.1109/5326.669549

Filename

669549