A neural network based fuzzy set model for organizational decision making

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.