Methods of Constructing the Neural Network Models of Regulators for Controlling a Dynamic Object with Smooth Monotonous Behavior

Emaletdinova, L. Yu.; Kabirova, A. N.

doi:10.3103/s1068799819020065

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

Neural Network Model for Detecting Network Scanning Attacks

Panischev¹,

Makridin²,

Katasev³

et al. 2020

International Journal of Engineering Research and Technology

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Section: Discussionmentioning

confidence: 99%

Neural Network Model for Detecting Network Scanning Attacks

Panischev¹,

Makridin²,

Katasev³

et al. 2020

International Journal of Engineering Research and Technology

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“…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]. As the data analysis tool, it is relevant to use a neuro-fuzzy model formed as a result of training a fuzzy neural network (FNN) [23].…”

Section: Introductionmentioning

confidence: 99%

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

Panischev¹,

Ahmedshina²,

Kataseva³

et al. 2020

International Journal of Engineering Research and Technology

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This paper poses and solves the problem of developing the upto-date neuro-fuzzy model of formation of a knowledge base for an intelligent decision-making support system for selection of geological and technical measures in oil fields. The analysis of the traditional approach to the formation of fuzzy knowledge bases made it possible to reveal its shortcomings associated with the need to attract experts, structure and formalize the system of decision-making rules by them. This process is laborious and does not always provide an acceptable result. To eliminate the disadvantages of the traditional approach, we proposed an approach to the automatic formation of a knowledge base based on the construction of a neuro-fuzzy model of a collective of fuzzy neural networks. We formulated the requirements in view of the formed fuzzy rules. We developed a scheme for using the rules of the knowledge base to solve the problem of selecting geological and technical measures in oil fields. We tested the generated knowledge base on the example of solving the problem of selecting geological and technical measures for various wells of the Feofanovskoye Field. Application of the knowledge base made it possible to select a list of optimal measures for given wells. The experiment results are satisfactory and are confirmed by the positive expert assessments, selecting geological and technical measures at this field.

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Methods of Constructing the Neural Network Models of Regulators for Controlling a Dynamic Object with Smooth Monotonous Behavior

Cited by 2 publications

References 5 publications

Neural Network Model for Detecting Network Scanning Attacks

Neural Network Model for Detecting Network Scanning Attacks

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

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