SOME MODEL APPLICATIONS OF THE FUZZY SETS THEORY IN DECISION-MAKING

Abstract

The objects of fuzzy sets applications are presented by probabilistic neuron ensemble, which is practiced on a large scale in neurophysiology, neuropsychology, and neurocybernetics. Numerous studies of the neuron ensemble structure and their functional properties have shown that there is a probability of neurons taking part in the formation of such ensembles dynamic mosaic of activity, but the ensemble membership of these neurons cannot be determined with high precision. In terms of fuzzy sets theory, the neuron ensemble is a certain primary neuron unit of inexact composition, providing the elementary information processing. One of the difficulties of practical use of the fuzzy sets theory in solving many complex problems, particularly in neurophysiology, is the elaboration of corresponding methods of estimation of the numerical values of membership function. This chapter describes simple equations for the estimation of the numerical values of membership function of each cell in different parts of nervous cells ensembles. The criterion of the effectiveness of such method is the absence of essential difference between the values of membership function that were theoretically calculated and those received in the experiments relative to central excited area or to surrounded area of inhibited neurons.