Parameter identification of dynamic models using a Bayes approach

The Bayesian method of statistical analysis has been applied to the paramet er identification problem. A method is presented to identify parameters of dynamic models with the Bayes estimators of measurement frequencies. This i s based on the solution of an inverse generalized evaluate problem. The sto chastic nature of test data is considered and a normal distribution is used for the measurement frequencies. An additional feature is that the enginee r's confidence in the measurement frequencies is quantified and incorporate d into the identification procedure. A numerical example demonstrates the e fficiency of the method.