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- W2766190252 abstract "Abstract The paper presents an approach to the statistical linearization of input/output mappings of stochastic systems, with emphasizing the significance of applying consistent (in accordance to the A.N. Kolmogorov terminology) measures of dependence of random values. Within such a problem statement, the criterion function is constructed by applying the Renyi symmetric divergence leading to the corresponding consistent measure of dependence, the Renyi symmetric mutual information, which combines both properties of the Kullback-Leibler divergence (expansion on the sum of corresponding summands) and the Renyi entropy (logarithm of integral instead of integral of logarithm), what is of particular importance, from the system identification point of view, under constructing sample estimates, especially those of based on observation of dependent data." @default.
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- W2766190252 date "2017-07-01" @default.
- W2766190252 modified "2023-09-23" @default.
- W2766190252 title "Applying Measures of Dependence Based on the Symmetric Rényi Divergence to the Statistical Linearization" @default.
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- W2766190252 doi "https://doi.org/10.1016/j.ifacol.2017.08.1557" @default.
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