Abstract
In Chap. 5 we start with the identification of static linear systems, that is, no dynamics are involved. The output of a static system depends only on the input at the same instant and thus shows instantaneous responses. In particular, the so-called least-squares method is introduced. As will be seen in Chaps. 5 and 6, the least-squares method for the static linear case forms the basis for solving nonlinear and dynamic estimation problems. For the analysis of the resulting estimates, properties like bias and accuracy are treated. Special attention is paid to errors-in-variables problems, which allow noise in both input and output variables, to maximum likelihood estimation as a unified approach to estimation, in particular well-defined in the case of normal distributions, and to bounded-noise problems for cases with small data sets.
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