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 under- and overestimation.