Neuro-fuzzy model of fuzzy rules formation for objects state evaluation in conditions of uncertainty

Abstract

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.

Neural Network Model for Detecting Network Scanning Attacks

“…Thus, the model is adequate and suitable for use in intelligent detection systems for network scanning attacks [22]. As a direction for further research, it is proposed to develop other models and evaluate the results obtained on the basis of other methods of data mining [23][24][25][26][27][28]. In addition, the actual construction and practical use of intelligent decision support systems [29][30][31][32][33][34] to identify and prevent network attacks on computer systems.…”

Neurofuzzy Model of Formation of Knowledge Bases for Selection of Geological and Technical Measures in Oil Fields

“…As can be seen from the figure, the GTM selection in the oil fields requires the use of fuzzy production rules of the corresponding type. The paper proposes to use the following type of rules [18]:…”

“…At the same time, the proposed solution is interpretable and understandable for a person, which increases its reliability and confidence level among the DM. The knowledge base used in this case can be formed by an expert method [16,17], or the process of its formation can be automated [18,19]. In the latter case, it is currently relevant to use Data Mining methods to form fuzzy knowledge bases [20][21][22].…”

Fuzzy Multi-Criterial Choice of Geological and Technical Measures

“…To represent the expert's knowledge about the choice of geological and technical measures based on the values of geological and physical parameters, it is proposed to use the modernized form of fuzzy products "IF <list of conditions> THEN <decision taken>" [22]. To take into account the difficulties listed earlier in making decisions, a modified model of the following form was proposed [23]:…”