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Re: MORLAB (Model Order Reduction LABoratory) v3.0

From: Julien Bect
Subject: Re: MORLAB (Model Order Reduction LABoratory) v3.0
Date: Tue, 3 Oct 2017 13:21:17 +0200
User-agent: Mozilla/5.0 (X11; Linux x86_64; rv:52.0) Gecko/20100101 Thunderbird/52.3.0

Le 27/09/2017 à 23:06, Sergei Steshenko a écrit :
From: Julien Bect <address@hidden>
To: help-octave Octave <address@hidden>
Sent: Tuesday, September 26, 2017 10:54 AM
Subject: MORLAB (Model Order Reduction LABoratory) v3.0

Seen on the "NA Digest" list :

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Sujet : [NADIGEST] NA Digest, V. 17, # 26
Date : Mon, 25 Sep 2017 23:28:25 -0400
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Pour : address@hidden

From: Steffen W. R. Werner address@hidden Date: September 20, 2017
Subject: Software Release: MORLAB 3.0 Version 3.0 of the MORLAB (Model Order 
Reduction LABoratory) toolbox
has been released. The toolbox is a collection of MATLAB/OCTAVE
routines for model order reduction of linear dynamical systems based
on the solution of matrix equations. The implementation is based on
spectral projection methods, e.g., methods based on the matrix sign
function and the matrix disk function. The toolbox contains implementations for 
standard and descriptor
- Modal truncation
- Balanced truncation
- Bounded-real balanced truncation
- Positive-real balanced truncation
- Balanced stochastic truncation
- Linear-quadratic-Gaussian balanced truncation
- H-infinity balanced truncation
- Hankel-norm approximation
Also, matrix equation solvers based on the matrix sign function as
well as further subroutines for linear dynamical systems can be found
in the MORLAB toolbox. For more details on this software, see:
Help-octave mailing list


I am not familiar with this stuff, but the "model reduction" suggests the SW 
might realize what I need - the description of what I need is right below.

Suppose I have an IIR filter expressed in its standard form in, say, Z-domain: 
H(z) = B(z)/A(z) where B(z) and A(z) are polynomials in my case of the same 
order. The way I obtain the polynomials is through fitting experimental 
frequency response, and by the very construction of the method I use the order 
of the polynomials is definitely higher than needed to fit the curve.

So, I need to reduce the order of the polynomials - are these Model Order 
Reduction algorithms capable of doing what I need provided I change the 
representation of the IIR into a matrix form demanded by the algorithms ?

Hi Sergei,

I can't help you, but perhaps the author of MORLAB (in CC) can ?


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