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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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Submitted on : Wednesday, May 24, 2006 - 3:53:40 PM
Last modification on : Friday, February 4, 2022 - 3:15:07 AM
Long-term archiving on: : Monday, April 5, 2010 - 12:12:11 AM


  • 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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