TABLE OF CONTENTS The time-series methods have gained considerable acceptance in system identification literature in view of their inherent simplicity and flexibility [1 3]. These techniques provide external descriptions of systems under study and lead to parsimonious, minimum parameterisation representation of the process. The accurate determination of the dynamic order of the time-series models is a necessary first step in system identification.
Many statistical tests are available in the literature which can be used to find the model order for any given process. Selection of a reliable and efficient test criterion has been generally elusive, since most criteria are sensitive to statistical properties of the process. These properties are often unknown. Validation of most of the available criteria has generally been via simulated data. However, these order determination techniques have to be used with practical systems with unknown structures and finite data. It is therefore necessary to validate any model order criterion using a wide variety of data sets from differing dynamic systems.
The aspects of time-series/transfer function modelling are included here from the perspective of them being special cases of specialised representations of the general parameter estimation problems. The coefficients of time-series models are the parameters, which can be estimated by using the basic least squares, and maximum likelihood methods discussed in Chapters 2 and 3. In addition, some of the model selection criteria are used in EBM procedure for parameter estimation discussed in Chapter 7, and hence the emphasis on model selection criteria in the present chapter.
