Fit Gaussian Peak and non-linear curve Fit on small data differ from the PEAK of origin made

Similar Questions

• Non-linear curve Fit error 20068?

  Trying to do a simple 3-exponential decay curve nonlinear.  I have a copy of this work in the other screws, but it doesn't seem to work in this VI.  I use "Scalablemultiexponentialdecay" of Altenbach as a VI/template model.  Data that I'm trying to adapt are a series simple decomposition RC, yes I realize that should not have 3 exponential components, it's just the model data.  I am able to adapt with the Fit exponential function built in to the Math palette, but which only works if the input bounds parameter is wired and with rational constraints.  However, I get an error on the adjustment of the non-linear curve that I understand not (photo attached) indicating that an INF or NaN is done in the settings.  I don't see where this is going...

  Any questions or help is appreciated.

  

  

  I thought so, only 0 s for the 2nd two exponentials.  But, if I replace all the guess seededes with the same as the simple exponential Installer, it converges without error.   I guess I didn't know it was that sensitive.  Thank you.

• Non-linear curve fit the model of reference file

  Hello.

  I use the VI of the non-linear curve adjustment in order to adapt the data. The reference to the fitting VI model I use is included in the attachment. You can see that I have a few constant wireline, like 4, 2500 and 1. I want to do this constant variables I'll change before each curve corresponds, because actually in my problem this variables I know before the adjustment and they are constantly changing, and for the moment this made VI just to test.

  The problem I have is that I can't enter the values of this variable to my main VI, where I also call the VI was nonlinear. The scheme of connection of refernce VI made must be changed in order to be recognized by the VI was nonlinear. I tried to use a table to transfer variables, but if I use one, it recognizes the variables as parameters of editing and he's trying to install as well in the adjustment process, and it gives me erroneous results.

  Any ideas how I can add the values of variables?

  Thank you very much.

  Kind regards

  Nikola comedy

  To provide additional data randomly in VI of model, you must use the entrance of 'data' (it is a variant and so can contain anything you want!). Just create any type of data you want (generally a cluster if there are several values of different data types) that contains all the values, convert them to a Variant in the main VI and the variant of wire to crimp her. In the model, you convert the variant return to data, to the constant help of cluster, such as defined in the main as VI 'type' (simply right click cluster in the main VI... wire Create constant... move the constant in VI of model). Now, to unbundle the different values and use them anywhere inside your model.

• Non-linear curve fit (distribution of blackbody radiation)

  Hello

  In my measurements I would estimate the temperature from the spectra of light emitted through the Planck law of the distribution of the black body radiation. I tried to get my data with non-linear curve fit, but I encountered some problems:

  1.) function is not properly, because of the distribution of different adjusted data form and input data values.

  (2) when tracing distribution of Planck by using the best shape parameter, the plot is different from the theoretical distribution of given temperature. (My data comes from the source of temperature 3100K, best setting made is 1130K, but fit is different from the theoretical distribution of 1 130 K)

  

  When I get a few simple equations, everything works, so I'm not sure of what could be a problem.

  Many thanks for any advice.

  Ivan

  Quickly, giving once more on this, it seems to me that one of your constant if four orders of large magnitude.

  You get a very good fit with 3.74177E - 16 instead of 3.74177E-12, see picture. (You divide your theoretical curves by 10000, but you aren't in your formula!)

  

• apply restrictions for the non-linear curve fit

  Hello Forum users,.

  I am currently working on a VI control which is supposed to create a specific model of pressure inside a hollow tube to provide a test environment for pressure sensors. The details are many and complicated, so let's say I am sure that my formula to calculate this profile according to the pressures inside the hollow rings around this tube will work.

  To find the correct pressure for each ring values, I've linked to a model VI containing this form to Lev. - Mar algorithm (the non-linear curve adjustment) and let it run.

  Technically, there is no problem and Lev - Mar find values to adjust the function for the values (not perfectly sure, but close enough).

  The problem is, however, that, since the device, once suitable values for the positioning and size of the rings are found, must be built, the simulated pressure rings perhaps intersect not between them. To apply this rule, I added a check to my VI of model and if the values passed to Lev - Mar breaking the rules, the VI model gives a matrix of zeros to follow him (I tried an empty array, but that only leads to error messages).

  This solution did not work. Lev - Mar seems to ignore these cases always looks for values that break my rules (and if I put these values through my model VI, I get a matrix of zeros, as expected, so the audit seems to work).

  Perhaps I misunderstood the algorithm of Lev - Mar and it does not actually check each possible defined coefficients of finds.

  Is it possible to adapt to any function of a set of values while keeping the predefined boundary conditions?

  Oh, before I forget:

  I use LabVIEW version 8.2 in the Institute, because the workshop systems is not installed 8.5 for some reason any.

  Thanks in advance

  Thaliur

  Hi Thaliur,

  Thanks for posting on our forums.

  I understand your request you expect the algorithm to ignore a case to all zeros in the table. However, it is not implemented like this.

  Good news is, its source code can be edited and you could save your own personalized version of the algorithm of screws it is to you that you just add a check for a matrix of zeros or simply pass another parameter which indicates only a case of "broken rules". Then you would not have to continue the calculation.

  If I misunderstood your question, please clarify this. You can also post a code for further explanation, if you wish.

  Good luck with the project!

  Peter

• Non-linear curve shape VI don't, get error-20068

  Hi, I have a problem of raw data to a formula of mounting. The formula is f (x) = - ax + b + c (x/d) ^ e. I use the non-linear curve Fit VI, Lev - Mar: the formula string. I'm able to calculate 4 5 parameters (a, b, c and d). I can't calculate, but it should be around 10. When you run the VI, it gives me "error-20068 occurred at NI_Gmath.lvib:LM get new coefficients.vi:1. " "Analysis: input parameters has at least one element that is Inf, NaN, DBL_MAX or DBL_MIN"

  It is tracing the equation based on the initial settings without actually finding the best adjustment of the parameters. I also tried using the VI version to create the formula, but it gives me the same error. What can I do to avoid this error? I have attached the vi and a data file.

  

  Some of your top X values are negative. Remove them and things will work fine.

  (You get a negative signal for the exponentiation, which is allowed if the entry is complex or the exponent is an integer. This isn't the case here, where the exponent is not an integer. The exponent is an integer in the first function call, but not more once, trying to calculate the partial derivatives of the model. Try with a hypothesis of 9.9 instead of 10, and it will fail immediately)

• The non-linear curve slope

  Hi all

  How can I get the slope of a particular point in the non-linear curve? I use Lev - Mar curve fitting is not linear.  The slope is to determine the dynamic resistance of the current-voltage curve of diode if dynamic resistance = 1/slope. Kindly help...

  
  

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• coefficients variables semi in a non-linear curve fit

  Hi all

  I use a curved Lev - Mar not linear adjustment vi to fit a custom to my data function. The function is a set of Gaussian functions and an offset. I mean to constrain the width of one or more of the Gaussian curves in the function. I can do this by changing the values in the equation in the model description in the block diagram, but how to do this easily through the front?

  These width values will not be floated in the adjustment.

  Thank you!

  Unfortunately, the limited version was added in LabVIEW 8.5.  You could use the 'data' variant to pass the value of the stress sigma model and adjust the location and amplitude values. This means that your entry of Lev - Mar estimate does not include the values of sigma.  The value of sigma fixed wire entry "data".  Then depending on your model take data entry (type variant), cast to a value double precision and insert into the locations in your model formula.

  -Jim

• The non-linear curve fit lev mar problem

  Hi, I have a set of 10000 readings recorded every second. My goal to draw these vs time readings (1-10000 s) in logarithmic scale and adjust the exponential curve that results with my model equation: a1 * exp (t * b1) + a2 * exp (t * b2) + a3 * exp (t * b3) + a4 * exp (t * b4) and get the values of the coefficients (a1 to a4, b1 - b4). I changed the non-linear adjustment of lev - mar.vi according to my model. However, I ran into a problem.  I get the following: error-20041 occurred at LM.vi:5 to get on the curve of the NI_Gmath.lvlib:Nonlinear Possible reason (s): the system of equations can be solved because the input matrix is singular. I can't work on why I get this error. I enclose 3 files: the data file (values of Y), X = 1-10000; coating not get my model and vi vi.

  I'm using Labview 8

  I would appreciate your help and suggestions! Thank you very much in advance. ANU

  Hello

  @Jim-thank you very much... ur modified vi helped a lot... but a strange thing on the adjustment is that it depends a lot on the estimation of the coefficients... my model should have values of 'a' coeffs in the order of 10 ^-7 and 'b' should be higher around 10 ^-1. The initial proposal is amended the best coefficients made vary accordingly.

  I don't really know if this can b fixed... I enclose my vi.

  VI: - non - linear adjustment model, exponential branch.

  data - pol.txt

  I appreciate you all!

• Need help with the non-linear curve - fit

  Hello

  "I enclose you a VI that calls the function of nonlinear smoothing" Lev - Mar "with two slightly different formulas.

  On my machine (Win7, LV2011) adjustment is a straight line when bool example FP is set to FALSE.

  This is expected behavior?

  Is it possible to get a good fit, with the coefficient of 0.97 and _without_ increase in the number of points?

  Thanks in advance!

  I've just implemented the formula to the fitting VI.  I did not review the calculation you did for "raw" data

  Review: The first value of x is equal to zero. To get a phase shift of ~pi/2 a good first guess for c is 1.57.  With a value close to that, it gives a good fit for each choice of c.

  

  Lynn

• Adjustment of the non-linear curve - maximum iterations exceeded (error-23026)

  Hello

  In my application, I use the curve non-linear adjustment VI (Levenberg-Marquardt) to adapt the data acquired continuously using a task DAQmx. Often in the course of the implementation of the instrument, the curves are not yet "adaptable" and max iterations set in the parameters of expiration is reached.

  It's OK but the problem is that it generates an error (-23026) that stops the execution of the VI.

  How can I ignore this error and continue to run the VI until the termination is controlled by the setting of tolerance?

  Kind regards

  Bernard

  Hi Bernard,.

  Need to wire the error output and handle the error programmatically.

• Questions on the non-linear curve Lem - Mar vi

  Hello, I try to adapt my data for my model and stuck now.

  Before you ask, here is my model to fit:

  t = k * I + Ja + uw + T * tanh(A*w)

  I have the same numbers of sets of data to I, a, w, t.

  I want to know the best settings games made of K, J, u, T,.

  I know it is underconstrained and there could be many solutions but it's okay anyway.

  The solution is use in my application.

  I tried to build a running model vi and vi, and the vi runs and gives me a very crude answer together.

  Which means, he's only 10 ~ 12 iterations and it ends and gives the final solution, even if I put an end criteria very strictly.

  I want to run at least 10000 or more iterations to adjust to my data, but it doesn't seem to give a lead.

  Can I request a notice in this situation?

  Here's my vi and data sets.

  The real problem is built-in model.vi. Partial derivatives are false. In fact each partial derivative is calculated correctly, but they are in the wrong order in table f'(X,a). The parameters in a table are k u T A J. Derivatives are dt/dJ, dk/dt, dt /, dt/dt and dt / dA. Note that dJ and dk derivatives are in reverse order compared to the parameters. When I change the order, made it very similar to the measured data and the residue is much smaller.

  Several times should not explicitly calculate partial derivatives. VI connection will perform digital differentiation if f'(X,a) table is empty.

  Most of the mathematical functions are polymorphic. They can take pictures as inputs as well as scalars. The model fitting VI is not the loop when you take advantage of polymorphism.

  Lynn

  

• FFT and differ from the values overall btwn VI and tiara

  Hi all

  I have an application that displays live readings of vibrations which the spectrum and the overall vibration level.  Everything my generation of report is made in the DIAdem scripts, so it is essential that all values calculated and displayed on the side of LabVIEW are the values calculated and reported in DIAdem.  The VI and Script, when the entrance to the PDM attachment, attached give me values significantly different to the size of the FFT and the overall value of RMS.  Can anyone spot what I'm doing wrong?

  Thank you

  Scott

  Hello

  This is an excellent response of R & D. I felt that it would be better to put it on the web, then keep it limited to your service request.

  The difference has to do with the window of correction. The results are the same if you choose rectangluar (or not) window.

  Usually a window takes a share of the singnal away, so in the first place, the result has a different shape and is smaller compared to train with rectangular window. This can be fixed with a specific factor for each window.

  There are two possible corrections. Random and periodic.

  -Periodic is used if you have pure sinusoidal signals and you want to measure the peak value. A typical use case is the window of flattop is designed for this type of calculations.   The periodic correction is too great if you want to add values to calculate the total RMS signal or the sum of a certain frequency band.

  -Random is correct the signal back to the correct overall RMS value and must be used in all other cases.

  The periodic correction for Hanning window is a factor of two. The correction is randomly about 1.633. Tiara will give the same result as LabVIEW if you use the following type of correction:

  FFTWndCorrectTyp = "periodic".

  In the world of the FFT analysis, the different corrections are very often not obvious and hidden to the user. Unfortunately, it is also the case in LabVIEW. You can find a good example of explanation here:
  http://blog.Prosig.com/2009/09/01/amplitude-and-energy-correction-a-brief-summary/

  There are different factors for different window functions. If you go further in the FFT - VI you will find "window scaling VI. It has a «constant window» output With this, it is possible to obtain the correction values for window functions.

  I think also, periodical is false in most cases, when Hanning is used, because Hanning is the best for the sum of the values of the RMS in the frequency bands and the results are bad without a correction at random.

  Hope that helps.

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