Resumen
The subject of the article's research is the formalization of unstructured or semistructured verbal information for a fuzzy production system. The purpose of the work is to develop a method of constructing membership functions for fuzzy sets of terms of a linguistic variable that will allow formalizing unstructured or semistructured verbal information for fuzzy production systems. The following tasks are solved in the article: to develop a method of constructing the membership functions to determine the sequence of stages: the stage of modeling verbal information in the form of digraphs, the stage of constructing the order relation on the elements of this model, the step of determining a linguistic variable based on the created model, and determining the functions of fuzzy sets of linguistic terms variable. Methods are used: graph theory, mathematical induction, fuzzy modeling. Results obtained: a method for constructing the membership function of linguistic variables that formalizes unstructured or semi-structured qualitative information for fuzzy production systems is developed. For this purpose, the process of constructing the membership function has been broken down into stages. The implementation of the first stage requires the creation of a model of unstructured or semistructured verbal information. Three models of information based on oriented trees are considered with increasing complexity. A model based on an acyclic oriented graph is considered as a generalization. Such a model is the basis for processing information that has a structure of greater complexity. The second stage provides a theoretical basis for constructing the order relation for the elements of the models under consideration. For the implementation of the third stage, a method of identifying the order elements on the basis of the positional system is proposed. Based on the ID of each ordered element, functions of fuzzy sets of terms of a linguistic variable are constructed. Appropriate procedures have been developed to implement the steps. Conclusions: application of the method will allow automating the assignment of vectors of input and output information, to automate the formation of fuzzy sets of terms of the corresponding linguistic variables, will allow to build fuzzy products as a knowledge base of a fuzzy production system, and to train such a system.