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.
Type de document :
[Research Report] RR-2063, INRIA. 1993
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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



  • HAL Id : inria-00074609, version 1


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



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