ABSTRACT
The chapters in Parts I and II laid down the foundations on models of discrete-time deterministic and stochastic processes, respectively, while Part III provided the paraphernalia for estimating these models. In Part IV (this part), beginning with this chapter, our goal is to collectively apply the concepts from the previous chapters to frame and solve identification problems. We shall begin by studying the different model types amenable for linear identification. Essentially we shall weave the descriptions of Chapters 4 and 5 with those of Chapters 9 to 11. The end result is a composite model that aims to describe both the deterministic and stochastic effects of the sampled-data system.
