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Titlebook: Interactive System Identification: Prospects and Pitfalls; Torsten Bohlin Book 1991 Springer-Verlag Berlin, Heidelberg 1991 Identification

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Structure identification,he purpose (see chapter 7 on “Validation techniques”). That may well be a structure that is simpler than one needed to describe the object as well as the data would allow. The purpose may not require as detailed a description as identification would actually be able to provide.
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Falsification techniques,ontradicted by data, and the falsification rule becomes . ∩ . (.) = ∅. Due to the randomness in the data the set .(.) is not unique and not necessarily the same for model and structure. Falsification techniques deal with the problems of finding suitable sets .(.) for the two cases.
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The experiment, to draw applicable conclusions from data obtained from the experiment. At the very best one may find a probabilistic model of the combined system of experimenter and object, but the problem is still to separate one from the other. This has to be done, whenever the purpose requires that one have a m
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The identification problem,s the Principle of Logical Empiricism, which yields a general procedure for refinement and falsification of hypotheses, and one has Bayes’ Rule, which provides a way to formalize the information in experiment data, and in this way ties what one wants to know to what one can observe from experiments.
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Modelling,s to bring detail into the descriptions of model structures .. That cannot be done without loss of generality. In fact, the whole purpose of modelling is to restrict the set of all possible models so much that the remaining set has so few degrees of freedom left, that identification will be able to
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