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.
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Original Russian Text © Yu.S. Popkov, A.Yu. Popkov, Yu.N. Lysak, 2014, published in Avtomatika i Telemekhanika, 2014, No. 5, pp. 83–90.
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Popkov, Y.S., Popkov, A.Y. & Lysak, Y.N. Estimating the characteristics of randomized dynamic data models (the Entropy-Robust Approach). Autom Remote Control 75, 872–879 (2014). https://doi.org/10.1134/S0005117914050063
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DOI: https://doi.org/10.1134/S0005117914050063