Determining the accuracy of a fuzzy model of the technology foresight / Kupchyn, / Komarov, / Borokhvostov, / Bilokur, / Kuprinenko, / Mishchenko, / Bohdanovych, / Kononov. (2022)
Ukrainian

English  Cybernetics and Systems Analysis   /     Issue (2022, 58 (3))

Kupchyn A., Komarov V., Borokhvostov I., Bilokur M., Kuprinenko A., Mishchenko Y., Bohdanovych V., Kononov O.
Determining the accuracy of a fuzzy model of the technology foresight

A method for checking the accuracy of prognostic models in the absence of experimental data for comparing the modeling results is presented. The developed neural network determines a technology class, which is compared with the results obtained using the fuzzy logic model. The model accuracy is determined by computing the root-mean-square error of the modeling and the correlation between the results obtained using the fuzzy logic model and the neural network. © 2022, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: critical technology foresight, fuzzy logic, model accuracy, modeling error, neural network, Engineering education, Fuzzy inference, Fuzzy neural networks, Mean square error, Critical technologies, Critical technology foresight, Fuzzy logic modeling, Fuzzy-Logic, Logic technology, Model errors, Modeling accuracy, Neural-networks, Prognostic modeling, Technology foresight, Computer circuits


Cite:
Kupchyn A., Komarov V., Borokhvostov I., Bilokur M., Kuprinenko A., Mishchenko Y., Bohdanovych V., Kononov O. (2022). Determining the accuracy of a fuzzy model of the technology foresight. Cybernetics and Systems Analysis, 58 (3), 72–82. doi: https://doi.org/10.1007/s10559-022-00470-1 http://jnas.nbuv.gov.ua/article/UJRN-0001323858 [In Ukrainian].


 

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