Journal of Mathematical Sciences

, Volume 231, Issue 5, pp 678–689 | Cite as

Selection of the Informative Input Parameters for the Inverse Neural-Network Models of Observed Systems

  • N. І. Obodan
  • N. А. Guk
  • A. S. Magas

We consider the problem of determination of the parameters of a measurement grid, which guarantees the exactness and stability of the solution of the inverse problem. The choice of the points of measurements is performed under the assumption of existence of the most informative data. We present the results illustrating the influence of the number of measurement points on the data of reconstruction of the parameters of the load function acting upon the cylindrical shell in a strip located along the length of the shell.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    F. R. Gantmakher, The Theory of Matrices, Vol. 1, Chelsea, New York (1959).Google Scholar
  2. 2.
    A. N. Gorban’, V. L. Dunin-Barkovskii, A. N. Kirdin, E. M. Mirkes, A. Yu. Novokhod’ko, D. A. Rossiev, S. A. Terekhov, M. Yu. Senashova, and V. G. Tsaregorodtsev, Neuroinformatics [in Russian], Nauka, Novosibirsk (1998).Google Scholar
  3. 3.
    E. M. Kiseleva and N. Z. Shor, Continuous Problems of the Optimal Partition of Sets: Theory, Algorithms, and Applications [in Russian], Naukova Dumka, Kiev (2005).Google Scholar
  4. 4.
    А. А. Samarskii and P. N. Vabishchevich, Numerical Methods for Solving Inverse Problems of Mathematical Physics [in Russian], Editorial URSS, Moscow (2004); English translation: de Gruyter, Berlin (2007).Google Scholar
  5. 5.
    A. N. Tikhonov and V. Y. Arsenin, Methods for the Solution of Ill-Posed Problems [in Russian], Nauka, Moscow (1979).Google Scholar
  6. 6.
    M. A. Arain, H. V. H. Ayala, and M. A. Ansari, “Nonlinear system identification using neural network,” in: Communications in Computer and Information Science, Vol. 281: B. S. Chowdhry, F. K. Shaikh, D. M. Akbar, and M. Aslam Ugaili (editors), Emerging Trends and Applications in Information Communication Technologies, Berlin–Heidelberg: Springer (2012), pp. 122–131.Google Scholar
  7. 7.
    S. Chen, S. A. Billings, and P. M. Grant, “Nonlinear system identification using neural networks,” Int. J. Control, 51, No. 6, 1191–1214 (1990).MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    N. Sakundarini, T. Zahari, S. H. Abdul-Rashid, R. A. Ghazilla, and J. Gonzales, “Multi-objective optimization for high recyclability material selection using genetic algorithm,” Int. J. Adv. Manuf. Technol., 68, No. 5-8, 1441–1451 (2013).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • N. І. Obodan
    • 1
  • N. А. Guk
    • 1
  • A. S. Magas
    • 1
  1. 1.Honchar Dnipropetrovs’k National UniversityDnipropetrovs’kUkraine

Personalised recommendations