BibTeX
@MISC{Ninness_orthonormalbases,
author = {Brett Ninness and Fredrik Gustafsson},
title = {Orthonormal Bases for System Identification},
year = {}
}
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Abstract
In this paper we present a general and very simple construction for generating complete orthonormal bases for system identification. This construction provides a unifying formulation of orthonormal bases since the common FIR and recently popular Laguerre and Kautz model structures are restrictive special cases of our construction. A distinguishing feature of our construction is that it can generate basis vectors having nearly arbitrary magnitude frequency responses. These responses can be selected according to the prior information the user wishes to inject into the estimation problem. 1 Introduction This paper is concerned with the problem of estimating the dynamics of single input, single output linear time invariant systems on the basis of noisy sampled observations of their input-output response. One of the most popular existing methods for dealing with this problem involves modelling the system dynamics via a so-called ARX structure [4, 7]. A problem then is that it is difficult...
Keyphrases
system identification orthonormal base simple construction input-output response restrictive special case system dynamic so-called arx structure distinguishing feature estimation problem kautz model structure unifying formulation complete orthonormal base arbitrary magnitude frequency response single input prior information basis vector common fir popular laguerre
