Development of a method for assessment and forecasting of the radio electronic environment

Decision making support systems (DSS) are actively used in all spheres of human life. The system of the electronic environment analysis is not an exception. However, there are a number of problems in the analysis of the electronic environment, for example: the signals are analyzed in a complex electronic environment against the background of intentional and natural interference. Input signals do not match the standards, and their interpretation depends on the experience of the operator (expert), the completeness of additional information on a particular task (uncertainty condition). The best solution in this situation is found in the integration with the data of the information system analysis of the electronic environment, artificial neural networks and fuzzy cognitive models. Their advantages are also the ability to work in real time and quick adaptation to specific situations. The article develops a method for assessing and forecasting the electronic environment. Improving the efficiency of evaluation information processing is achieved through the use of evolving neuro-fuzzy artificial neural networks; learning not only the synaptic weights of the artificial neural network, the type and parameters of the membership function. The efficiency of information processing is also achieved through training in the architecture of artificial neural networks; taking into account the type of uncertainty of the information that has to be assessed; synthesis of rational structure of fuzzy cognitive model. It reduces the computational complexity of decision-making; has no accumulation of learning error of artificial neural networks as a result of processing the information coming to the input of artificial neural networks. The example of assessing the state of the electronic environment showed an increase in the efficiency of assessment at the level of 15–25 % on the efficiency of information processing

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DEVELOPMENT OF AN ADVANCED METHOD OF FINDING SOLUTIONS FOR NEURO-FUZZY EXPERT SYSTEMS OF ANALYSIS OF THE RADIOELECTRONIC SITUATION

EUREKA Physics and Engineering ◽

10.21303/2461-4262.2020.001353 ◽

2020 ◽

Vol 4 ◽

pp. 78-89

Author(s):

Hennadii Pievtsov ◽

Oleksandr Turinskyi ◽

Ruslan Zhyvotovskyi ◽

Oleg Sova ◽

Oleksii Zvieriev ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Information Processing ◽

Expert Systems ◽

System Analysis ◽

High Efficiency ◽

Electronic Environment ◽

Neuro Fuzzy ◽

Fuzzy Expert Systems ◽

Artificial Neural

Nowadays, artificial intelligence has entered into all spheres of our life. The system of analysis of the electronic environment is not an exception. However, there are a number of problems in the analysis of the electronic environment, namely the signals. They are analyzed in a complex electronic environment against the background of intentional and natural interference. Also, the input signals do not match the standards due to the influence of different types of interference. Interpretation of signals depends on the experience of the operator, the completeness of additional information on a specific condition of uncertainty. The best solution in this situation is to integrate with the data of the information system analysis of the electronic environment and artificial neural networks. Their advantage is also the ability to work in real time and quick adaptation to specific situations. These circumstances cause uncertainty in the conditions of the task of signal recognition and fuzzy statements in their interpretation, when the additional involved information may be incomplete and the operator makes decisions based on their experience. That is why, in this article, an improved method for finding solutions for neuro-fuzzy expert systems of analysis of the electronic environment is developed. Improving the efficiency of information processing (reducing the error) of evaluation is achieved through the use of neuro-fuzzy artificial neural networks that are evolving and learning not only the synaptic weights of the artificial neural network, but also the type and parameters of the membership function. High efficiency of information processing is also achieved through training in the architecture of artificial neural networks by taking into account the type of uncertainty of the information that has to be assessed and work with clear and fuzzy products. This reduces the computational complexity of decision-making and absence of accumulation of an error of training of artificial neural networks as a result of processing of the arriving information on an input of artificial neural networks. The use of the proposed method was tested on the example of assessing the state of the electronic environment. This example showed an increase in the efficiency of assessment at the level of 20–25 % on the efficiency of the processing information

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MATHEMATICAL SUPPORT FOR INDUCTION SOLDERING CONTROL BASED ON INTELLIGENT METHODS OF INFORMATION PROCESSING

СИСТЕМЫ УПРАВЛЕНИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ ◽

10.36622/vstu.2021.85.3.015 ◽

2021 ◽

pp. 81-87

Author(s):

А.В. Милов

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Artificial Neural Networks ◽

Information Processing ◽

Mathematical Models ◽

Technological Process ◽

Artificial Neural ◽

Intelligent Methods

В статье представлены математические модели на основе искусственных нейронных сетей, используемые для управления индукционной пайкой. Обучение искусственных нейронных сетей производилось с использованием многокритериального генетического алгоритма FFGA. This article presents mathematical models based on artificial neural networks used to control induction soldering. The artificial neural networks were trained using the FFGA multicriteria genetic algorithm. The developed models allow to control induction soldering under conditions of incomplete or unreliable information, as well as under conditions of complete absence of information about the technological process.

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Assessing Artificial Neural Networks (ANNS) Adequacy Against Econometric Models for Decision Making Approaches in Banking Industry

Contributions to Economics - Business Performance and Financial Institutions in Europe ◽

10.1007/978-3-030-57517-5_7 ◽

2020 ◽

pp. 105-116

Author(s):

Sotirios J. Trigkas ◽

Konstantinos J. Liapis

Keyword(s):

Neural Networks ◽

Decision Making ◽

Artificial Neural Networks ◽

Banking Industry ◽

Econometric Models ◽

Artificial Neural

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Using Artificial Neural Networks to Determine the Extent to Which the Private Sector in the KSA Benefit from Quantitative Methods in Decision-Making

المجلة العلمیة للدراسات التجاریة والبیئیة ◽

10.21608/jces.2021.204613 ◽

2021 ◽

Vol 12 (3) ◽

pp. 175-212

Author(s):

FUAD Awwad

Keyword(s):

Neural Networks ◽

Decision Making ◽

Artificial Neural Networks ◽

Private Sector ◽

Quantitative Methods ◽

Artificial Neural

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What do artificial neural networks tell us about the genetic structure of populations? The example of European pig populations

Genetics Research ◽

10.1017/s0016672309000093 ◽

2009 ◽

Vol 91 (2) ◽

pp. 121-132 ◽

Cited By ~ 6

Author(s):

NATACHA NIKOLIC ◽

YOUNG-SEUK PARK ◽

MAGALI SANCRISTOBAL ◽

SOVAN LEK ◽

CLAUDE CHEVALET

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Genetic Model ◽

Unsupervised Classification ◽

Genetic Distances ◽

Population Diversity ◽

Factorial Correspondence Analysis ◽

Self Organizing Map ◽

Additional Information ◽

Artificial Neural

SummaryGeneral and genetic statistical methods are commonly used to deal with microsatellite data (highly variable neutral genetic markers). In this paper, the self-organizing map (SOM) that belongs to the unsupervised artificial neural networks (ANNs) was applied to analyse the structure of 58 European and two Chinese pig populations (Sus scrofa) including commercial lines, local breeds and cosmopolitan breeds. Results were compared with other unsupervised classification or ordination methods such as factorial correspondence analysis, hierarchical clustering from an allele sharing distance and the Bayesian genetic model and with principal components analysis and neighbour joining from allelic frequencies and genetic distances between populations. Like other methods, SOMs were able to classify individuals according to their breed origin and to visualize similarities between breeds. They provided additional information on the within- and between-population diversity, allowed differences between similar populations to be highlighted and helped differentiate different groups of populations.

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