.png){kind=icon}
.png){kind=icon}
Return
Minimal partial realization from generalized orthonormal basis function expansions
.png){kind=icon}
PRE
AI
DOI：10.1016/S0005-1098(01)00247-3.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A solution is presented for the problem of realizing a discrete-time LTI state-space model of minimal McMillan degree such that its first N expansion coefficients in terms of generalized orthonormal basis match a given sequence. The basis considered. also known as the Hambo basis, can be viewed as a generalization of the more familiar Laguerre and two-parameter Kautz constructions, allowing general dynamic information to be incorporated in the basis. For the solution of the problem use is made of the properties of the Hambo operator transform theory that underlies the basis function expansion. As corollary results compact expressions are found by which the Hambo transform and its inverse can be computed efficiently. The resulting realization algorithms can be applied in an approximative sense. for instance. for computing a low-order model from a large basis function expansion that is obtained in an identification experiment. (C) 2002 Elsevier Science Ltd. All rights reserved.
Keywords:
realization theory
partial expansions
algorithms
state-space realization
transforms
all-pass filters
interpolation
system identification
model approximation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.