Geodesic
Sequential application of the hierarchy analysis method and associative training of a neural network in examination problems
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematičeskoe modelirovanie i programmirovanie, Tome 10 (2017) no. 3, pp. 142-147 Cet article a éte moissonné depuis la source Math-Net.Ru

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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. 
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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 Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematičeskoe modelirovanie i programmirovanie, Tome 10 (2017) no. 3, pp. 142-147. http://geodesic.mathdoc.fr/item/VYURU_2017_10_3_a11/

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