Optimal sequential deduction and isolation of changes in stochastic systems

Igor Nikiforov 1
1 AS - Signal Processing and Control
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, INRIA Rennes
Abstract : The purpose of this paper is to give a new statistical approach to the change diagnosis (detection/isolation) problem. The change detection problem has received extensive research attention. On the contrary, change isolation is mainly an unsolved problem. We consider a stochastic dynamical system with aburpt changes and investigate the multihypothesis extension of Lorden's results. We introduce a joint criterion of optimality for the detection/isolation problem and then design a change detection/isolation algorithm. We also investigate the statistical properties of this algorithm. We prove a lower bound for the criterion in a class of sequential change detection/isolation algorithms. It is shown that the proposed algorithm is asymptotically optimal in this class. The theoretical results are applied to the case of additive changes in linear stochastic models.
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Rapport
[Research Report] RR-2063, INRIA. 1993
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https://hal.inria.fr/inria-00074609
Contributeur : Rapport de Recherche Inria <>
Soumis le : mercredi 24 mai 2006 - 15:53:40
Dernière modification le : mercredi 16 mai 2018 - 11:23:13
Document(s) archivé(s) le : lundi 5 avril 2010 - 00:12:11

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  • HAL Id : inria-00074609, version 1

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Igor Nikiforov. Optimal sequential deduction and isolation of changes in stochastic systems. [Research Report] RR-2063, INRIA. 1993. 〈inria-00074609〉

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