Asymmetrical Conditional Bayes Parameter Identification for Control Engineering

One of the main problems in control engineering practice results from unavoidable errors in specifying parameters existing in the object model and the necessity to deal with the unwanted phenomena arising as a result. In this article, a Bayes methodology considering both asymmetrical and conditional aspects is applied for this purpose, with the application of kernel estimators methodology. Use of the Bayes rule enables minimum potential losses to be assumed, while the asymmetry of the occurring loss function also enables the inclusion of different results for underand overestimation. A conditional approach allows researchers to obtain a more precise result thanks to using information entered as the fixed (e.g., current) values of conditioning factors of continuous and=or binary (also categorical) type. The nonparametric methodology of statistical kernel estimators frees the procedure from arbitrary assumptions concerning the forms of distributions characterizing both the parameter under investigation and conditioning factors. The generalizations introduced here also allow different relevance of particular random sample elements to be taken into account.