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1 2005

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An algorithm for Bayes parameter identification with quadratic asymmetrical loss function

Kulczycki P., Mazgaj A.

Control and Cybernetics

2005

Vol. 34, no 4

1127-1148

The paper deals with the estimation problem of model parameter values, in tasks where overestimation implies results other than underestimation, and wliere losses arising from this can be described by a quadratic function with different coefficients characterizing positive and negative errors. In the approach presented, the Bayes decision rule was used, allowing for minimizing potential losses. Calculation algorithms were based on the theory of statistical kernel estimators, which frees the method from distribution type. The result constitutes a complete numerical procedure enabling effective calculation of the value of an identified parameter or - in the multidimensional case - the vector of parameters. The method is aimed at both of the main contemporary approaches to uncertainty modeling: probabilistic and fuzzy logic. It is universal in nature and can be applied in a wide range of tasks of engineering, economy, sociology, biomedicine and other related fields.