Vestnik Yuzhno-Ural'skogo Universiteta. Seriya Matematicheskoe Modelirovanie i Programmirovanie
RUS  ENG    JOURNALS   PEOPLE   ORGANISATIONS   CONFERENCES   SEMINARS   VIDEO LIBRARY   PACKAGE AMSBIB  
General information
Latest issue
Archive
Submit a manuscript

Search papers
Search references

RSS
Latest issue
Current issues
Archive issues
What is RSS



Vestnik YuUrGU. Ser. Mat. Model. Progr.:
Year:
Volume:
Issue:
Page:
Find






Personal entry:
Login:
Password:
Save password
Enter
Forgotten password?
Register


Vestnik YuUrGU. Ser. Mat. Model. Progr., 2020, Volume 13, Issue 4, Pages 66–80 (Mi vyuru572)  

Programming & Computer Software

Prediction of the integrated indicator of quality of a new object under the conditions of multicollinearity of reference data

S. B. Achlyustin, A. V. Melnikov, R. A. Zhilin

Institute of the Ministry of Internal Affairs, Voronezh, Russian Federation

Abstract: Prediction of a new object state at a lack of the known characteristics and estimates of quality indicators of a number of studied objects (a set of reference data) often leads to the problem of multicollinearity of basic data. We propose the following three ways to overcome this problem relating to the sphere of data mining: use a ridge regression, train with the teacher a two-layer neural network, consecutive adapt a single-layer neural network. Also, we compare characteristics of the proposed ways. In the ridge regression method, the introduction of a regularizing term into the LMS equation gives an approximate solution with a sufficient degree of accuracy. A disadvantage of use of the two-layer neural network “feed-forward backprop” and the procedure of training with the teacher “train” is that adjusted weights of the neural network take chaotic (and even negative) values that contradicts a common practice of examination. The following features are revealed: considerable dispersion of weights and shifts of a neural network, ambiguity of the solution due to the choice of random initial conditions, strong dependence on a training algorithm. In order to overcome this shortcoming, we propose a transition to consecutive adaptation of a single-layer neural network with fixing shifts of neurons at zero level.

Keywords: examination of objects, prediction, multiple regression, ridge regression, regularization, neural network, training of model, adaptation.

DOI: https://doi.org/10.14529/mmp200406

Full text: PDF file (277 kB)
References: PDF file   HTML file

UDC: 004.891
MSC: 68T05, 62J07, 68Q32
Received: 23.07.2019
Language:

Citation: S. B. Achlyustin, A. V. Melnikov, R. A. Zhilin, “Prediction of the integrated indicator of quality of a new object under the conditions of multicollinearity of reference data”, Vestnik YuUrGU. Ser. Mat. Model. Progr., 13:4 (2020), 66–80

Citation in format AMSBIB
\Bibitem{AchMelZhi20}
\by S.~B.~Achlyustin, A.~V.~Melnikov, R.~A.~Zhilin
\paper Prediction of the integrated indicator of quality of a new object under the conditions of multicollinearity of reference data
\jour Vestnik YuUrGU. Ser. Mat. Model. Progr.
\yr 2020
\vol 13
\issue 4
\pages 66--80
\mathnet{http://mi.mathnet.ru/vyuru572}
\crossref{https://doi.org/10.14529/mmp200406}


Linking options:
  • http://mi.mathnet.ru/eng/vyuru572
  • http://mi.mathnet.ru/eng/vyuru/v13/i4/p66

    SHARE: VKontakte.ru FaceBook Twitter Mail.ru Livejournal Memori.ru


    Citing articles on Google Scholar: Russian citations, English citations
    Related articles on Google Scholar: Russian articles, English articles
  • Number of views:
    This page:30
    Full text:6
    References:2

     
    Contact us:
     Terms of Use  Registration to the website  Logotypes © Steklov Mathematical Institute RAS, 2021