Expert rules refinement by solving fuzzy relational equations

Optimization of fuzzy classification knowledge bases using improving transformations

“…The papers [19][20][21][22][23] proposed methods for simplifying the process of tuning a fuzzy knowledge base for the specified (unknown) output classes and input terms.…”

“…Application of the composite transformation allows reducing the number of tuning parameters by solving Z optimization problems for 2m boundaries of output classes [19][20][21]. 2N variables for boundaries of -parameters of the rules are subject to tuning.…”

“…The problem of min-max clustering is solved by generating composite rules in the form of interval solutions of the trend system of equations [19][20][21]. The number of rules in a class is determined by the number of solutions, and the granularity is determined by intervals of values of input variables in rules.…”

“…Improving transformations of fuzzy classification knowledge bases are based on solving fuzzy relational equations [17,18]. As improving transformations, the following is proposed: transition to a composite model for selecting the number of output classes and rules [19][20][21]; transition to a relational model for selecting the number of input terms [22,23].…”

Classification rule hierarchical tuning with linguistic modification based on solving fuzzy relational equations

“…In [14][15][16], the method for tuning of classification rules based on the inverse logic inference has been proposed. In [14][15][16], the primary relational model has been used that did not require primary selection. The hedging threshold of the primary terms has been determined by solutions of the system of fuzzy relational equations with extended max-min composition.…”