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Neural
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Posted on 09-08-09 5:07
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Has anyone come across with the following:
I need to calculate parameters a1, n1 ... a3, n3 of the following equation (linear, say):a1, a2, a3 are coefficients;n1, n2, n3 are exponential ones. Y = X1 (a1 * V^{n1} + a2 * V^{n2} + a3 * V^{n3})
where Y, X1, and V are known values.
Will it be possible to do so in MATLAB? if so, I would highly appreciate if you could let me know the procedure.
Else, any other approach will be warmly welcomed.
Thx in advance.
Last edited: 08-Sep-09 05:12 AM
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GuitarDaku
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Posted on 09-08-09 12:49
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Do "lookfor parameter". Looks like something which should be done in matlab. If you can linearize that equation and change into matrix form, that could be one way.
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F22
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Posted on 09-08-09 1:04
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I think you need the Curve Fitting Toolbox, or may be try statistical model fitting in JMP or Minitab
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Samayayatri
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Posted on 09-08-09 1:35
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Aiya!!! This is a linear combination of exponential functions! I wonder if the fminsearch function will come in handy. I've never used it though. You'll have to set up your function as follows: Y = X1 (a1 * V^{n1} + a2 * V^{n2} + a3 * V^{n3})
Let P=Y./X1;
So now you'll have:
P=a1*V^{n1} + a2*V^{n2}+a3*V^{n3};
Beyond that, I can't say much.
If you could provide the scenario in which you're trying to estimate this, perhaps it would help?
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samaeBajii
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Posted on 09-08-09 2:09
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It would still be degenerate, I think. More detail might help.
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Neural
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Posted on 09-08-09 6:44
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Yeap, I thought the same, f22. Either CFT or system identification approach might work. Is any one using Siemens PTI PSS/E tool for power systems dynamic simulation?? -- thx guys for your inputs.
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nepaliraja
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Posted on 09-09-09 4:57
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Try least square. Here is a nonlinear solverhelp lsqnonlin LSQNONLIN solves non-linear least squares problems. LSQNONLIN attempts to solve problems of the form: min sum {FUN(X).^2} where X and the values returned by FUN can be x vectors or matrices.choose FUN = Y - X1 (a1 * V^{n1} + a2 * V^{n2} + a3 * V^{n3}) and put known constants Y, X1 and V. The x vectors would be [a1, a2, a3, n1, n2, n3]. It will find the least square i.e. FUN^2 for different possible of x. Minimum FUN^2 will be 0 so you will get Y = X1 (a1 * V^{n1} + a2 * V^ + {n2}a3 * V^{n3}) by changing a's and n's.
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Samayayatri
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Posted on 09-09-09 9:12
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Neural
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Posted on 09-18-09 8:07
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. Thx nepaliraja for the valuable info. (sorry for the late response). I used "polyfit" to calculate the coefficients and keeping it 3rd degree polynomial. Meanwhile, this is working. Cheers ! Happy Dashain to you all Sajha friends.
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