2001
DOI: 10.1016/s0005-1098(00)00174-6
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Computationally efficient steady-state multiscale estimation for 1-D diffusion processes

Abstract: Conventional optimal estimation algorithms for distributed parameter systems have been limited due to their computational complexity. In this paper, we consider an alternative modeling framework recently developed for large-scale static estimation problems and extend this methodology to dynamic estimation. Rather than propagate estimation error statistics in conventional recursive estimation algorithms, we propagate a more compact multiscale model for the errors. In the context of 1-D di!usion which we use to … Show more

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Cited by 4 publications
(2 citation statements)
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“…In this case, V may take positive values because of the last two terms. Hence, no stability conclusion can be drawn from the Lyapunov function candidate (10). (a) But, further assume that a and b are bounded and 1 , a b L ∈ .…”
Section: Case (2)mentioning
confidence: 99%
See 1 more Smart Citation
“…In this case, V may take positive values because of the last two terms. Hence, no stability conclusion can be drawn from the Lyapunov function candidate (10). (a) But, further assume that a and b are bounded and 1 , a b L ∈ .…”
Section: Case (2)mentioning
confidence: 99%
“…Distributed sensing and actuation are also assumed. Compared to the adaptive control/identification of finite dimensional systems, that of infinite dimensional systems is not well developed and has been recently studied [1][2][3][4][5][6][7][10][11][12][13][14][19][20]21,25,30,[32][33][34].…”
Section: Introductionmentioning
confidence: 99%