This article solves the problem of constructing a neuro-fuzzy model of fuzzy rules formation and using them for objects state evaluation in conditions of uncertainty. Traditional mathematical statistics or simulation modeling methods do not allow building adequate models of objects in the specified conditions. Therefore, at present, the solution of many problems is based on the use of intelligent modeling technologies applying fuzzy logic methods. The traditional approach of fuzzy systems construction is associated with an expert attraction need to formulate fuzzy rules and specify the membership functions used in them. To eliminate this drawback, the automation of fuzzy rules formation, based on the machine learning methods and algorithms, is relevant. One of the approaches to solve this problem is to build a fuzzy neural network and train it on the data characterizing the object under study. This approach implementation required fuzzy rules type choice, taking into account the processed data specificity. In addition, it required logical inference algorithm development on the rules of the selected type. The algorithm steps determine the number and functionality of layers in the fuzzy neural network structure. The fuzzy neural network training algorithm developed. After network training the formation fuzzyproduction rules system is carried out. Based on developed mathematical tool, a software package has been implemented. On its basis, studies to assess the classifying ability of the fuzzy rules being formed have been conducted using the data analysis example from the UCI Machine Learning Repository. The research results showed that the formed fuzzy rules classifying ability is not inferior in accuracy to other classification methods. In addition, the logic inference algorithm on fuzzy rules allows successful classification in the absence of a part of the initial data. In order to test, to solve the problem of assessing oil industry water lines state fuzzy rules were generated. Based on the 303 water lines initial data, the base of 342 fuzzy rules was formed. Their practical approbation has shown high efficiency in solving the problem.

Ключевые слова: нейросетевая модель, пупиллометрия, зрачковая реакция, предрейсовый медицинский осмотр, функциональное состояние опьянения водителя, принятие решений Работа выполнена при финансовой поддержке Министерства образования и науки РФ в рамках государственного задания по проекту № 8.6141.2017/8.9.

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We propose a prototype of the classifier of electronic documents for the decision support system in the field of economic justice. The system uses both wellknown text analytics algorithms and an original algorithm based on an artificial neural network. A text mining model has been developed to classify court documents to determine the category (class) of a statement of claim. A preliminary analysis of court documents and the selection of significant features were carried out. To choose the best way of solving problem of document classification we implemented Bayesian classification algorithm, k nearest neighbor algorithm and decision trees algorithm. All used algorithms show results with errors on the same sample corpus of texts. To improve the accuracy of classification, an original model based on an artificial neural network was developed, which shows an unmistakable determination of the type of document on a test sample for a number of classes of lawsuits in arbitration proceedings.

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