Abstract:
We propose a new approach to finding dependencies between small volumes of input and output data based on randomized dynamic models and density estimation for the distributions of their parameters. Randomized dynamic models are defined by functional Volterra polynomials. To construct robust nonparametric estimation procedures, we develop an entropybased approach that employs functionals of generalized informational Boltzmann and Fermi entropies.
Presented by the member of Editorial Board:A. I. Kibzun
Citation:
Yu. S. Popkov, A. Yu. Popkov, Yu. N. Lysak, “Estimating the characteristics of randomized dynamic data models (the entropy-robust approach)”, Avtomat. i Telemekh., 2014, no. 5, 83–90; Autom. Remote Control, 75:5 (2014), 872–879
\Bibitem{PopPopLys14}
\by Yu.~S.~Popkov, A.~Yu.~Popkov, Yu.~N.~Lysak
\paper Estimating the characteristics of randomized dynamic data models (the entropy-robust approach)
\jour Avtomat. i Telemekh.
\yr 2014
\issue 5
\pages 83--90
\mathnet{http://mi.mathnet.ru/at9095}
\transl
\jour Autom. Remote Control
\yr 2014
\vol 75
\issue 5
\pages 872--879
\crossref{https://doi.org/10.1134/S0005117914050063}
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Linking options:
https://www.mathnet.ru/eng/at9095
https://www.mathnet.ru/eng/at/y2014/i5/p83
This publication is cited in the following 3 articles:
Svetlana Solodusha, Yuliya Kokonova, Oksana Dudareva, “Integral Models in the Form of Volterra Polynomials and Continued Fractions in the Problem of Identifying Input Signals”, Mathematics, 11:23 (2023), 4724
Yu. S. Popkov, Yu. A. Dubnov, “Entropy-robust randomized forecasting under small sets of retrospective data”, Autom. Remote Control, 77:5 (2016), 839–854
Y. S. Popkov, A. Y. Popkov, B. S. Darkhovskiy, “Parallel Monte Carlo for entropy-robust estimation”, Math. Models Comput. Simul., 8:1 (2016), 27–39