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

Keywords: artificial intelligence; electronic environment; intelligent systems; decision-making support systems

Abstract

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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Author Biographies

Ruslan Zhyvotovskyi , Central Scientific Research Institute of the Army of the Armed Forces of Ukraine

Research Department of  Development of Anti-Aircraft Missile Systems and Complexes

Oleg Sova , Military Institute of Telecommunications and Informatization named after Heroes of Kruty

Department of Automated Control Systems

Oleksii Zvieriev, Central Scientific Research Institute of the Army of the Armed Forces of Ukraine

Research Department of Development of Anti-Aircraft Missile Systems and Complexes

Boris Lanetskii , Ivan Kozhedub Kharkiv National Air Force University

Scientific Center

Andrii Shyshatskyi , Central Scientific Research Institute of the Army of the Armed Forces of Ukraine

Research Department of Electronic Warfare Development

References

Kuchuk, N., Mohammed, A. S., Shyshatskyi, A., Nalapko, O. (2019). The method of improving the efficiency of routes selection in networks of connection with the possibility of self-organization. International Journal of Advanced Trends in Computer Science and Engineering, 8 (1). 1–6. Available at: http://www.warse.org/IJATCSE/static/pdf/file/ijatcse01812sl2019.pdf

Sova, O., Turinskyi, O., Shyshatskyi, A., Dudnyk, V., Zhyvotovskyi, R., Prokopenko, Y. et. al. (2020). Development of an algorithm to train artificial neural networks for intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 1 (9 (103)), 46–55. doi: https://doi.org/10.15587/1729-4061.2020.192711

Mikhaylov, R. L., Makarenko, S. I. (2013). Estimating Communication Network Stability Under the Conditions of Destabilizing Factors Affecting it. Radiotehnicheskie i telekommunikatsionnye sistemy, 4, 69–79.

Bodyanskiy, E. V., Strukov, V. M., Uzlov, D. J. (2017). Generalized metrics in the problem of analysis of multidimensional data with different scales. Zbirnyk naukovykh prats Kharkivskoho universytetu Povitrianykh Syl, 3, 98–101.

Semenov, V. V., Lebedev, I. S. (2019). Processing of signal information in problems of monitoring information security of unmanned autonomous objects. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 19 (3), 492–498. doi: https://doi.org/10.17586/2226-1494-2019-19-3-492-498

Zhou, S., Yin, Z., Wu, Z., Chen, Y., Zhao, N., Yang, Z. (2019). A robust modulation classification method using convolutional neural networks. EURASIP Journal on Advances in Signal Processing, 2019 (1). doi: https://doi.org/10.1186/s13634-019-0616-6

Shaheen, E. M., Samir, M. (2013). Jamming Impact on the Performance of MIMO Space Time Block Coding Systems over Multi-path Fading Channel. REV Journal on Electronics and Communications, 3 (1-2). doi: https://doi.org/10.21553/rev-jec.56

Malik, S., Kumar, S. (2017). Optimized Phase Noise Compensation Technique using Neural Network. Indian Journal of Science and Technology, 10 (5), 1–6. doi: https://doi.org/10.17485/ijst/2017/v10i5/104348

Rotshteyn, A. P. (1999). Intellektual'nye tehnologii identifikatsii: nechetkie mnozhestva, neyronnye seti, geneticheskie algoritmy. Vinnitsa: Universum-Vinnytsia, 295.

Mazhara, O. A. (2015). Treat algorithm implementation by the basic match algorithm based on CLIPS programming environment. Elektronnoe modelirovanie, 37 (5), 61–75.

Bolotova, S. Yu., Makhortov, S. D. (2011). Relevant backward inference algorithms based on solving the production logical equations. Iskusstvenniy intellekt i prinyatie resheniy, 2, 40‒50.

Zhyvotovskyi, R., Shyshatskyi, A., Petruk, S. (2017). Structural-semantic model of communication channel. 2017 4th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T). doi: https://doi.org/10.1109/infocommst.2017.8246454


Abstract views: 27
PDF Downloads: 24
Published
2020-07-24
How to Cite
Pievtsov, H., Turinskyi, O., Zhyvotovskyi , R., Sova , O., Zvieriev, O., Lanetskii , B., & Shyshatskyi , A. (2020). DEVELOPMENT OF AN ADVANCED METHOD OF FINDING SOLUTIONS FOR NEURO-FUZZY EXPERT SYSTEMS OF ANALYSIS OF THE RADIOELECTRONIC SITUATION. EUREKA: Physics and Engineering, (4), 78-89. https://doi.org/10.21303/2461-4262.2020.001353
Section
Mathematical Sciences