A.A. Stoorvogel and J.H. Van Schuppen (1995). System identification with information theoretic criteria. CWI Report BS-R9513. Home/Search Document Details and Download
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Information Criteria For Residual Generation And Fault.. - Michèle.. (1997) (4 citations) (Correct)
....filters (and not parity checks) can be found in [22] where H 2 or H1 norms of the noise to residual transfer function are minimized. Another reference is [47] H1 norm is also used in [23] It is interesting to note that this norm has strong connections with information theoretic concepts [56]. Irisa Information criteria for FDI 11 3 Basic statistical tools In this section, we describe key classical statistic tools for detection and isolation in the basic case of a Gaussian vector 6 (X = 0) The case of Gaussian regression vectors is addressed in section 4. We give several basic ....
A.A. Stoorvogel and J.H. Van Schuppen (1995). System identification with information theoretic criteria. CWI Report BS-R9513.
Approximation Problems with the Divergence Criterion for .. - Stoorvogel, van Schuppen Self-citation (Stoorvogel Van schuppen) (Correct)
....of at most a prespecified order such that the divergence between the probability measure associated with the output process of this system and the probability measure associated with the given process is as small as possible. This problem was formulated by the second author in [15] and restated in [12]. Two approximation problems are discussed in this paper. The first approximation problem concerns a pair of finite dimensional Gaussian random variables. This problem is motivated by the main approximation step of the subspace algorithm. The optimal approximation is to perform a transformation to ....
....so, establishing whether an infimum is unique and, if not, to classify all infima. The problem has been motivated above. The divergence criterion is related to the likelihood function as is well known. Apparently H. Akaike in [1, 2, 3] first published about this relation. In this regard, see also [12]. The problem is also motivated by the subspace algorithm for the approximation of stationary Gaussian processes. The nucleus of the subspace identification algorithm is an algorithm for the approximation problem of Gaussian random variables. The latter problem is like Problem 3.4, but the ....
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A.A. Stoorvogel and J.H. van Schuppen. System identification with information theoretic criteria. In S. Bittanti and G. Picci, editors, Identification, adaptation, learning, pages 289--338. Springer, Berlin, 1996.
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