The Localization Procedures of the Vector of Weighting Coefficients for Fuzzy Models of Choice

The author analyzes the localization procedures of the vector of weighting coefficients which are based on presenting the function of value by additive reduction adapted to fuzzy models of choice.

The Localization Procedures of the Vector of Weighting Coefficients on the Set of Teaching Experts in the Tasks of Consuming

In terms of binary relations the author analyses the task of an individual consumers’ choice on the teaching excerpts set. It is suggested to analyse the function of consumer’s value as additive reduction. For localization of the vector of weighting coefficients of additive reduction the procedures based on metrics of object distance towards the ideal point are suggested.

Optimal Decision Rules in Logical Recognition Models

The task of smooth and stable decision rules construction in logical recognition models is considered. Logical regularities of classes are defined as conjunctions of one-place predicates that determine the membership of features values in an intervals of the real axis. The conjunctions are true on a special no extending subsets of reference objects of some class and are optimal. The standard approach of linear decision rules construction for given sets of logical regularities consists in realization of voting schemes. The weighting coefficients of voting procedures are done as heuristic ones or are as solutions of complex optimization task. The modifications of linear decision rules are proposed that are based on the search of maximal estimations of standard objects for their classes and use approximations of logical regularities by smooth sigmoid functions.