Geodesic
Classification of air situation using deep neural networks and fuzzy inference
Nečetkie sistemy i mâgkie vyčisleniâ, Tome 13 (2018) no. 2, pp. 113-125

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A method for classifying the state of the air situation is proposed. Its basis is a deep neural network that solves the problem of reducing the dimension of the input vector of features, and a fuzzy inference machine that provides an assessment of the possibility of matching the feature vector of each of the operational situations. The result of the classifier is the possibilities of all states of the air situation at the current time. The classifier is demonstrated on a model example.
Keywords: air situation state, classification, possibility theory, deep neural network, fuzzy variable, soft computing, fuzzy inference. 
@article{FSSC_2018_13_2_a1,
     author = {V. I. Arefiev and A. B. Talalaev and S. V. Sorokin and A. V. Yazenin},
     title = {Classification of air situation using deep neural networks and fuzzy inference},
     journal = {Ne\v{c}etkie sistemy i m\^agkie vy\v{c}isleni\^a},
     pages = {113--125},
     publisher = {mathdoc},
     volume = {13},
     number = {2},
     year = {2018},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/FSSC_2018_13_2_a1/}
}
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V. I. Arefiev; A. B. Talalaev; S. V. Sorokin; A. V. Yazenin. Classification of air situation using deep neural networks and fuzzy inference. Nečetkie sistemy i mâgkie vyčisleniâ, Tome 13 (2018) no. 2, pp. 113-125. http://geodesic.mathdoc.fr/item/FSSC_2018_13_2_a1/