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From: "Rajiv Singh" <rajiv_singh@msn.com>
Newsgroups: comp.soft-sys.matlab
Subject: Re: Linear parametric identification
Date: Mon, 23 Feb 2009 09:47:49 -0500
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Your model has one "measured" input and one output. However, there is also a 
noise input which you could think of an unmeasured input. When you perform 
an estimation, you not only estimate a "measured" model G, but also a 
"noise" model H, according to equation:

y = Gu+He

H is the transfer function between the unmeasured (noise) input e and the 
output y. H explains the component of the output that could not be captured 
by G.  H is also called a disturbance model. The inputs are "u" (measured) 
and "e" (unmeasured).

When you do TF(arx221), this operation converts the noise input channel (e) 
into a regular input. Hence the number of inputs in the resulting model 
becomes 2. If you just need "G", the transfer function between measured 
input and output, you should do:

g2 = tf(arx221('m'))

This operation separates out the measured component (G) and converts only 
that component into a TF object.

HTH,
Rajiv


"AsimV" <asimvod@gmail.com> wrote in message 
news:dcb69d15-194c-40d1-acb9-49000bd8f1c8@l16g2000yqo.googlegroups.com...
> Hello to all,
>
> I'm experimenting with linear parametric identification methods. I
> have tested this methods when output data contains noise. I simulated
> noise by random number generator.
> Can you please explain to me what does it mean when one gets the
> following result:
> g2 = tf(arx221)
>
> Transfer function from input "u1" to output "y1":
>   -0.4777 z + 0.4677
> ------------------------
> z^2 - 0.9353 z - 0.01695
>
> Transfer function from input "v@y1" to output "y1":
>      0.01454 z^2
> ------------------------
> z^2 - 0.9353 z - 0.01695
>
> Data object for identification is formed from one input and one output
> vector. It is SISO system. How to inperpret input "v@y1" to output
> "y1"? What does it mean?
>
> Thank you