Abstract
Our departure point is the evolution equation of a Markov process. It describes the changes in the transition probability as time passes. We compare the transition probability for a priori model with the actual transition probability of the observed process to detect a mismatch between the expected and the measured data. To translate this idea into an algorithm, we characterise the involved measures by their moments. Specifically, a linear dynamic system is put forward that describes the evolution of moments. As the last result, we define a moment divergence as the means of computing the distance between two sequences of moments. We see the work as a step towards merging model-driven and data-driven concepts in control engineering. To elucidate the concepts introduced, we have incorporated several simple examples.
Originalsprog | Engelsk |
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Bogserie | IFAC-PapersOnLine |
Vol/bind | 53 |
Udgave nummer | 2 |
Sider (fra-til) | 1974-1979 |
Antal sider | 6 |
ISSN | 2405-8963 |
DOI | |
Status | Udgivet - 2020 |
Begivenhed | 21th IFAC World Congress - Berlin, Tyskland Varighed: 12 jul. 2020 → 17 jul. 2020 |
Konference
Konference | 21th IFAC World Congress |
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Land/Område | Tyskland |
By | Berlin |
Periode | 12/07/2020 → 17/07/2020 |