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On the Choice of Norms in System Identification
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
1994 (engelsk)Inngår i: Proceedings of the 10th IFAC Symposium on System Identification, 1994, Vol. 2, s. 103-108Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In this paper we discuss smooth and sensitive norms for prediction error system identification when the disturbances are magnitude bounded. Formal conditions for sensitive norms, which give an order of magnitude faster convergence of the parameter estimate variance, are developed. However, it also is shown that the parameter estimate variance convergence rate of sensitive norms is arbitrarily bad for certain distributions. A necessary condition for a norm to be statistically robust with respect to the family F(C) of distributions with support [-C, C] for some arbitrary C>0 is that its second derivative does not vanish on the support. A direct consequence of this observation is that the quadratic norm is statistically robust among all lp-norms, p⩽2<∞ for F(C).

sted, utgiver, år, opplag, sider
1994. Vol. 2, s. 103-108
Emneord [en]
Convergence, Parameter estimation, System identification
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-94051ISBN: 9780080422251 (tryckt)OAI: oai:DiVA.org:liu-94051DiVA, id: diva2:630029
Konferanse
10th IFAC Symposium on System Identification, Copenhagen, Denmark, July, 1994
Tilgjengelig fra: 2013-06-18 Laget: 2013-06-16 Sist oppdatert: 2013-08-10

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