ABSTRACT
In the preceding chapter we learnt how to develop non-parametric models, i.e., models that do not assume any specific mathematical “structure” (equation of curves) for the response of the system. Here we study methods for estimating parametric models, which as we learnt in Chapter 4, are a result of parametrizing the responses of the plant and noise models. The basic idea here is to estimate the parameters instead of response coefficients. As we studied in Chapters 4 and 17, there are significant advantages to this approach, especially that of parsimony. In frequency domain, an added advantage is the fine resolution at which the FRF can be estimated using parametrized models - exactly along the lines on which parametric methods score over non-parametric counterparts for spectral density estimation. These advantages come at a price paid by the analyst in providing the information on delay, orders, etc. which is usually obtained through non-parametric identification.
