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Quantification of prior knowledge about global characteristics of linear normal model. (English) Zbl 0556.93070
The Bayesian approach to system identification requires from the user to collect and express his prior information about the identified system. The paper presents a way in which a global prior knowledge can be quantified. Two simple cases, pulse response smoothness and knowledge of the static gain of a system modelled by mixed autoregressive-regressive model, are elaborated in detail. These cases which are of practical importance illustrate main steps in the quantification. The significant and favourable effect of this built-in prior information on the start of self-tuning control is demonstrated on an example.

MSC:
93E12 Identification in stochastic control theory
62A01 Foundations and philosophical topics in statistics
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
93C05 Linear systems in control theory
93C40 Adaptive control/observation systems
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