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Least-squares fitting a square pulse

I am trying to fit a square pulse to data in the least-squares sense in Matlab. The parameters that are being optimized are the height, start, and end - x(1), x(2) and x(3) in the program, respectively. However, the least-squares program is only varying the height, for some reason. Why is this occurring? How else could I fit a square pulse using least-squares?
Although in this example the pulse would be easy to fit by inspection, for the purpose of my project, it is not an option.

Code:
%FITTING
xdata=0:0.1:10; 
ydata=-.05+.1*rand(1,numel(xdata))+heaviside1(xdata-1)-heaviside1(xdata-4);
x0=[.8 0.9 4];
[x,resnorm,residual,exitflag]=lsqcurvefit(@fit_pulse,x0,xdata,ydata)
fit=x(1).*(heaviside1(xdata-x(2))-heaviside1(xdata-x(3)));
plot(xdata,ydata,'or',xdata,fit,'b')

The program is calling the functions heaviside1 and fit_pulse:
%HEAVISIDE1 FUNCTION
function Y=heaviside1(x)
Y=zeros(size(x));
Y(x>0)=1;
end

%FIT_PULSE FUNCTION
function F=fit_pulse(x,xdata)
F=x(1).*(heaviside1(xdata-x(2))-heaviside1(xdata-x(3)));
end

Thanks
0
1/30/2012 3:01:11 PM
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"Riku" wrote in message <jg6bbn$d4s$1@newscl01ah.mathworks.com>...
> I am trying to fit a square pulse to data in the least-squares sense in Matlab. The parameters that are being optimized are the height, start, and end - x(1), x(2) and x(3) in the program, respectively. However, the least-squares program is only varying the height, for some reason. Why is this occurring? 
============

Probably because the gradient of the fitting function is zero with respect to
x(2) and x(3). If your current start and end lie in between your xdata sampling locations, tweaking x(2) and x(3) by small amounts produces no change in the value of fit_pulse. 


>How else could I fit a square pulse using least-squares?
============

Why does it have to be least squares? Can't you use some educated thresholding approach like,

threshold=max(ydata(:))/2;
idx=ydata>=threshold;
height=mean(ydata(idx));
area=sum(ydata)*(xdata(2)-xdata(1));
width=area/height;
centroid=dot(xdata,ydata)/sum(ydata);

x(1)=height;
x(2)=centroid-width/2;
x(3)=centroid+width/2;
0
mattjacREMOVE (3196)
1/30/2012 4:11:10 PM
 > Probably because the gradient of the fitting function is zero with respect to
> x(2) and x(3). If your current start and end lie in between your xdata sampling locations, tweaking x(2) and x(3) by small amounts produces no change in the value of fit_pulse. 

That makes sense. I looked in Help for lsqcurvefit, but couldn't find anything about changing the step size of the parameters of the fitted equation.

> Why does it have to be least squares? Can't you use some educated thresholding approach like,
> 
> threshold=max(ydata(:))/2;
> idx=ydata>=threshold;
> height=mean(ydata(idx));
> area=sum(ydata)*(xdata(2)-xdata(1));
> width=area/height;
> centroid=dot(xdata,ydata)/sum(ydata);
> 
> x(1)=height;
> x(2)=centroid-width/2;
> x(3)=centroid+width/2;

That works very well. The reasons I can think of why it needs to be least-squares are that I've been instructed to use that approach (university project); it may be more accurate for noisier data; it should work with more complicated fitted functions, that I will need to eventually use.
Thanks
0
1/30/2012 5:49:09 PM
"Riku" wrote in message <jg6l6l$isa$1@newscl01ah.mathworks.com>...
>  > Probably because the gradient of the fitting function is zero with respect to
> > x(2) and x(3). If your current start and end lie in between your xdata sampling locations, tweaking x(2) and x(3) by small amounts produces no change in the value of fit_pulse. 
> 
> That makes sense. I looked in Help for lsqcurvefit, but couldn't find anything about changing the step size of the parameters of the fitted equation.
================

That wouldn't be a good approach, anyway. LSQCURVEFIT just isn't designed to handle discontinuous/non-differentiable functions. 

If the solution I already gave you is insufficient, you should replace your definition of the heaviside function with something that is continuous and differentiable, so that your fitting function will likewise be continuous and differentiable. Perhaps the following


function Y=heaviside1(x)

Y=(x>=.1) + (x>=0 & x<=.1).*spline([0,.1],[0 0 1 0],x);
0
mattjacREMOVE (3196)
1/30/2012 7:23:09 PM
"Matt J" wrote in message <jg6qmt$62c$1@newscl01ah.mathworks.com>...
>
> If the solution I already gave you is insufficient, you should replace your definition of the heaviside function with something that is continuous and differentiable, so that your fitting function will likewise be continuous and differentiable. Perhaps the following
> 
> 
> function Y=heaviside1(x)
> 
> Y=(x>=.1) + (x>=0 & x<=.1).*spline([0,.1],[0 0 1 0],x);

Forget it... That won't do much.
0
mattjacREMOVE (3196)
1/30/2012 7:29:10 PM
Reply:

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Hei there, i have a problem calculating the estimated errors in parameters from a fit: i fit specific nonlinear function to a dataset. Afterwards i calculate the jacobian of the fitted parameters, however when i want to use nlparci it tells me that the matrix is ill conditioned and taht the matrix is singular to working precision. Is there any workaround for this? Can i determine the errors from something else, or should i transform, or calculate the jacobian in a specific way? Best wishes Clemens "Clemens" wrote in message <jkvcq3$qni$1@newscl01ah.mathworks.com>... > Hei there, > > i have a problem calculating the estimated errors in parameters from a fit: > > i fit specific nonlinear function to a dataset. Afterwards i calculate the jacobian of the fitted parameters, however when i want to use nlparci it tells me that the matrix is ill conditioned and taht the matrix is singular to working precision. > > Is there any workaround for this? Can i determine the errors from something else, or should i transform, or calculate the jacobian in a specific way? > A singular matrix here means that you CANNOT solve your problem in a way that will yield finite confidence limits. It means that some linear combination of the parameters is unspecified. Either you have defined the model poorly, or your data is so poor as to provide no information. Sorry, but sometimes there is no magic solution. John "John D'Errico"...

Recursive Least-Squares (RLS)
Hello, Would anyone know of where to find or have a C++ implmentation of this algorithm? I am presently using one of the implementations in the Matlab Filter Design Toolbox, but have found it to be slow. I'd like to try and speed up the processing if possible. Any suggestion or comments are greatly appreciated. Michael. "M. Wirtzfeld" <mwirtzfe@uwo.ca> wrote in message news:2dOdnfUB7YDaBz3enZ2dnUVZ_tmdnZ2d@mycybernet.net... > Hello, > > Would anyone know of where to find or have a C++ implmentation of this > algorithm? > > I am presently using one of the implementations in the Matlab Filter Design > Toolbox, but have found it to be slow. I'd like to try and speed up the > processing if possible. > > Any suggestion or comments are greatly appreciated. > > > Michael. > > > Did you expect it to be fast? How many parameters are you estimating? Ing Ing, No, I did not expect the algorithm to be fast. I understand what the algorithm is doing. I am applying it in a system modeling project and I would like have three tap values (64, 128, 256) for each forgetting-factor to get an idea of modeling performance. Michael. "Ingeniur" <ing@ingers.com> wrote in message news:dV2of.8129$vH5.414673@news.xtra.co.nz... > > "M. Wirtzfeld" <mwirtzfe@uwo.ca> wrote in message > news:2dOdnfUB7YDaBz3enZ2dnUVZ_tmdnZ2d@mycybernet.net... > > Hello, > > > > W...

labview: biphasic square wave pulse starting at 0
I'm new to Labview and am trying to figure out how to generate biphasic square wave pulses that start &amp; end at 0. I'm using a 33120A function generator / arb using GPIB. I've tried using the driver (the burst modulation and wave configuration VIs), but the pulses they give me start from the bottom of the square wave instead of from 0. Any thoughts? Thanks. ...

Re: GPLOT
When you ask for quad regression, it's not guaranteed you can get unique solution, so SAS back down and did the linear instead, also give the warning. That's my understanding. -----Original Message----- From: Lane, Jim [mailto:jim.lane@rbc.com] Sent: Thursday, July 09, 2009 11:59 AM To: Huang, Ya; SAS-L@LISTSERV.UGA.EDU Subject: RE: Re: GPLOT - Least-square solutions for the parameters are not unique. I asked for quadratic regression because that's what I wanted. I had already plotted linear by asking for that. My question is why won't SAS give me a quadratic regression when I explicitly asked for it? -----Original Message----- From: SAS(r) Discussion [mailto:SAS-L@LISTSERV.UGA.EDU] On Behalf Of Ya Huang Sent: 2009, July, 09 2:54 PM To: SAS-L@LISTSERV.UGA.EDU Subject: Re: GPLOT - Least-square solutions for the parameters are not unique. You basically asked gplot to fit quadratic regression, not linear regression, because you used i=rqclm95, where 'q' is for quadratic. If you really just need linear regression, change i=r instead. On Thu, 9 Jul 2009 14:43:51 -0400, Lane, Jim <jim.lane@RBC.COM> wrote: >Hi, All > >I'm getting a warning message from PROC GPLOT which I've never seen >before. My code looks like this: > >SYMBOL1 V=dot C=RED i=rqclm95 h=0.75 w=0.75; >axis1 value=(h=0.75) label=none; >axis2 value=(h=0.75) label=none order=('31may2008'd to '29jun2009'd by >month); proc gplot data=...

How can i figure this 'nonlinear least square problem'?
Hi all! Now i have this formula: min sum(|A*M1(n)*exp(j*theta*n)-M2(n)|^2) ,n =0,...,N-1; Unknown parameters A: complex value theta:real value Known data: M1(n):samples M2(n):samples I want to make that formula minimum,how can i do?I only know that it's a nonlinear least squares problem.please help me,thank you! On 15 Jun., 04:54, "flying " <flyingh...@163.com> wrote: > Hi all! > =A0 =A0 Now i have this formula: > =A0 =A0 =A0 =A0 =A0 min sum(|A*M1(n)*exp(j*theta*n)-M2(n)|^2) =A0,n =3D0,= ....,N-1; > Unknown parameters > A: complex value > theta:real value > > Known data: > M1(n):samples > M2(n):samples > > I want to make that formula minimum,how can i do?I only know that it's a = nonlinear least squares problem.please help me,thank you! Use lsqnonlin and define the functions f_k as f_k =3D Sqrt((A1^2+A2^2)*M1(k)*conj(M1(k)) - 2*real(M2(k)*(A1- i*A2)*conj(M1(k))*exp(-j*theta*n))+M2(k)*conj(M2(k))) where A=3DA1+i*A2. Thus there are three parameters (A1,A2 and theta) to be fitted. Best wishes Torsten. "flying" wrote in message <jre84f$opf$1@newscl01ah.mathworks.com>... > Hi all! > Now i have this formula: > min sum(|A*M1(n)*exp(j*theta*n)-M2(n)|^2) ,n =0,...,N-1; > Unknown parameters > A: complex value > theta:real value > > Known data: > M1(n):samples > M2(n):samples > > I want to ...

Least square inversion (normal equation vs Ax=b solution)
Hello All, Looking for some help regarding some linear algebra in Matlab (Linear Least Square Inversion). I have a problem of the form Ax=b+e where A is full rank and is sparse and e is some error. I also have a regularization matrix (R) and standard deviation error vector w I am trying to minimize: (b - A*x)'*diag(1/w)*(b - A*x) + y(R'*x')(R*x) (i.e. least square with regularization and known error (weights) where y is the scaling factor for the regularization component) This should be equal to solving the normal equation: x = inv(A'*diag(1/w)*A+y(R'*R))*A'*diag(1/w)*b I read somewhere that solving an inverse is not the best way so it would be better to reformulate the above problem as (Ax=b): x =[(A'*diag(1/w)*A+y(R'*R))]\[A'*diag(1/w)*b] But then I read another thing that says its better to avoid normal equations altogether which then would make the solution something like: x=[A*diag(1/w);yR]\[b*diag(1/w);zeros] However, the solution using the normal equation and the one above does not match. I would be glad if someone could point out my error. Thanks in advance. You forget the square-root When minimizing f(x) := (Ax-b)'*W*(Ax-b) + || R*x ||^2 W sdp matrix (W = diag(1./w) in your case) The solution is x = (A'*W*A + R'*R) \ (A'*W*b) % or sqrtW = sqrtm(W); % diag(1./sqrt(w)) in your case x = [sqrtW*A; R] \ [sqrtW*b; zeros(length(R),1)] % Bruno "Michal...

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