This article is cited in 1 paper

Short Notes

Sequential application of the hierarchy analysis method and associative training of a neural network in examination problems

O. S. Avsentiev^a, T. V. Meshcheryakova^a, V. V. Navoev<sup>b

a</sup> 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.

UDC: 519.816

MSC: 03D55

Received: 25.01.2017

Language: English

DOI: 10.14529/mmp170312

Bibliographic databases:

• 
•