Development of an algorithm to train artificial neural networks for intelligent decision support systems

Sova, Oleg; Turinskyi, Oleksandr; Shyshatskyi, Andrii; Dudnyk, Volodymyr; Zhyvotovskyi, Ruslan; Prokopenko, Yevgen; Hurskyi, Taras; Hordiichuk, Valerii; Nikitenko, Anton; Remez, Artem

doi:10.15587/1729-4061.2020.192711

Cited by 12 publications

(30 citation statements)

References 24 publications

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“…At this stage, the formation of KB on the basis of expressions (4)-(17). While converting the values of REE into fuzzy rules, the value of uncertainty about the sources of radio radiation is taken into account, according to expressions (10)-(12) [2].…”

Section: The Development Of An Improved Methods For Finding Solutions mentioning

confidence: 99%

“…The electronic environment, that is stored in computer memory in digital form, will be represented as a matrix R of the ( ) M N * dimension, and has the form [2,4]:…”

Section: Mathematical Formulation Of the Problem Of Analysis Of The Ementioning

confidence: 99%

“…This research is a further development of research that was conducted by the authors, aimed at developing the theoretical foundations for improving the efficiency of artificial intelligence systems and published earlier [1][2][3].…”

Section: Fig 2 the Comparison Of The Efficiency Of The Obtained Assmentioning

confidence: 99%

“…Nowadays, many areas of human activity use artificial intelligence approaches to solve important practical problems. Expert systems have been successfully used in complex technical systems to solve informal or poorly formalized tasks, such as training, diagnostics, forecasting, control and measurement [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…This class of intelligent information systems is characterized by the fact that they are able to model the process of thinking of the expert in making a decision and explain why this or that result was obtained. This is achieved by implementing the procedure of logical inference on formalized knowledge about the subject area, about the processes that take place in it, about the laws that govern these processes [2,4].…”

Section: Introductionmentioning

confidence: 99%

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Development of an Advanced Method of Finding Solutions for Neuro-Fuzzy Expert Systems of Analysis of the Radioelectronic Situation

Pievtsov

Turinskyi

Zhyvotovskyi

et al. 2020

EUREKA: Physics and Engineering

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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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Section: The Development Of An Improved Methods For Finding Solutions mentioning

confidence: 99%

“…The electronic environment, that is stored in computer memory in digital form, will be represented as a matrix R of the ( ) M N * dimension, and has the form [2,4]:…”

Section: Mathematical Formulation Of the Problem Of Analysis Of The Ementioning

confidence: 99%

Section: Fig 2 the Comparison Of The Efficiency Of The Obtained Assmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Development of an Advanced Method of Finding Solutions for Neuro-Fuzzy Expert Systems of Analysis of the Radioelectronic Situation

Pievtsov

Turinskyi

Zhyvotovskyi

et al. 2020

EUREKA: Physics and Engineering

Self Cite

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Enhancing Smishing Detection: A Deep Learning Approach for Improved Accuracy and Reduced False Positives

Mehmood,

Arshad,

Alawida

et al. 2024

IEEE Access

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Design of the Physical Fitness Evaluation Information Management System of Sports Athletes Based on Artificial Intelligence

Liu

Zhu

2022

Computational Intelligence and Neuroscience

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With the rapid development of science and technology in recent years, more and more researchers began to explore the basic disciplines of sports, namely, biomechanics and physiology, and further update and improve the traditional sports training theory. However, the developed countries, i.e., Europe and the United States, have dedicatedly worked on the effective role of physical training particularly in middle-class farmers in sports powers. They have realized that the continuous improvement of the physical training evaluation system is directly related to the reforming in the evaluation and monitoring methods of physical training. At the same time, there is a demand for athletes’ physical fitness evaluation, which requires a large amount of data support during the evaluation. These data collection and analysis are difficult and consume a lot of manpower. Therefore, this paper uses artificial intelligence technology to design the sports athletes’ physical fitness evaluation information management system to improve the comprehensiveness of system data collection and the accuracy of data analysis. Through the physical attenuation calculation and physical analysis of the system developed in this paper, data accuracy is high. The coach can make a training plan according to the physical consumption of athletes in sports training or competition, which can greatly improve the ability of athletes.

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Development of an algorithm to train artificial neural networks for intelligent decision support systems

Cited by 12 publications

References 24 publications

Development of an Advanced Method of Finding Solutions for Neuro-Fuzzy Expert Systems of Analysis of the Radioelectronic Situation

Development of an Advanced Method of Finding Solutions for Neuro-Fuzzy Expert Systems of Analysis of the Radioelectronic Situation

Enhancing Smishing Detection: A Deep Learning Approach for Improved Accuracy and Reduced False Positives

Design of the Physical Fitness Evaluation Information Management System of Sports Athletes Based on Artificial Intelligence

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