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This article is cited in 1 scientific paper (total in 1 paper)
Short Notes
Sequential application of the hierarchy analysis method and associative training of a neural network in examination problems
O. S. Avsentieva, T. V. Meshcheryakovaa, V. V. Navoevb a Voronezh Institute of the Ministry of Internal Affairs of Russia, Voronezh, Russian Federation
b Federal Service of National Guard Troops of the Russian Federation for the Sverdlovsk Region, Ekaterinburg, Russian Federation
Abstract:
We propose development of examination methodology
based on a sequential application of the MAI method (i.e., the hierarchy analysis method)
and associative training of neural networks. The proposed method is an
alternative to the usual methods to solve a direct examination problem.
We present a methodological approach to the examination problem. The approach
allows to save information about all objects and consider their indicators in total.
Therefore, there is the soft
maximum principle (softmax), based on the model of expert evaluations mixing. This
approach allows different
interpretations of the examination results, which save quality
unchanged overall picture of the examination object indicators ratio, and to get more reliable examination results, especially in cases where the objects characteristics are very different.
Keywords:
hierarchy analysis method; self-organizing neural networks; expert evaluations mixing.
Received: 25.01.2017
Citation:
O. S. Avsentiev, T. V. Meshcheryakova, V. V. Navoev, “Sequential application of the hierarchy analysis method and associative training of a neural network in examination problems”, Vestnik YuUrGU. Ser. Mat. Model. Progr., 10:3 (2017), 142–147
Linking options:
https://www.mathnet.ru/eng/vyuru393 https://www.mathnet.ru/eng/vyuru/v10/i3/p142
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Abstract page: | 105 | Full-text PDF : | 46 | References: | 28 |
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