Development of a complex method for finding a solution for neuro-fuzzy expert systems

Eastern-European Journal of Enterprise Technologies

Andrii Shyshatskyi¹  Valerii Hordiichuk²  Oleg Sova³  Oleksandr Gaman³  Mykhailo Sova³  Oleksandr Zhuk³  Dmytro Malitskyi⁴  Vitalii Shevchuk⁴  Vitalii Fedoriienko⁴  Andrii Kokoiko⁴ 

[1] Central Scientifically-Research Institute of Arming and Military Equipment of the Armed Forces of UkrainePovitroflotskyi ave., 28, Kyiv, Ukraine, 03168;Institute of Naval Forces National University “Odessa Maritime Academy”Hradonachalnytska str., 20, Odessa, Ukraine, 65029;Military Institute of Telecommunication and Information Technologies named after the Heroes of KrutyMoskovska str., 45/1, Kyiv, Ukraine, 01011;National Defense University of Ukraine named after Ivan CherniakhovskyiPovitroflotskyi ave., 28, Kyiv, Ukraine, 03049;

关键词: artificial intelligence;    operational situation;    intelligent systems;    decision support systems;   

DOI  :  10.15587/1729-4061.2020.216662

来源: DOAJ

【 摘 要 】

Artificial intelligence has become the backbone of modern decision support systems. This is why a complex method for finding solutions for neuro-fuzzy expert systems has been developed. The proposed complex method is based on a mathematical model for the analysis of the operational situation. The model makes it possible to determine the parameters of the analysis of the operational situation, their influence on the quality of assessment of the operational situation and to determine their number with units of measurement. An increase in the efficiency of information processing (error reduction) of the assessment is achieved by the use of evolving neuro-fuzzy artificial neural networks. Training of evolving neuro-fuzzy artificial neural networks is carried out by training not only synaptic weights of the artificial neural network, the type, parameters of the membership function, but also by applying the procedure for reducing the dimension of the feature space. The efficiency of information processing is also achieved by training the architecture of artificial neural networks; accounting for the type of uncertainty in the information to be assessed; work with both clear and fuzzy data. We achieved a reduction in computational complexity while making decisions; the absence of errors in training artificial neural networks as a result of processing information entering the input of artificial neural networks. The analysis of the operational situation as a whole occurs due to the improved clustering procedure, which allows working with both static and dynamic data. The proposed complex method was tested on the example of assessing the state of the operational situation. The mentioned example showed an increase in assessment efficiency at the level of 20–25 % in terms of information processing efficiency

【 授权许可】

Unknown