How can I estimate time constants in a Prony series without trial and error?

In summary, the conversation discusses trying to fit a prony series to experimental data using curve fitting. There is confusion about how to find the time constants without using a hit and trial method. The PDF provided in the link describes a least squares method for this type of problem, and it is suggested that at least 2*n data points are needed to obtain accurate estimates for the unknown variables.
  • #1
nellierd
11
0
Hi,

I am trying to fit a prony series to set of data(modulus and time). I want to use curve fitting to fit the experimental data set. I am confused about the time constants. Is there a way I can find the time constants without having to do it by hit and trial method. Is there a way by which I can estimate the time constants? Any help is appreciated.
 
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  • #2
The pdf in the below link describes a least squares method based on measured and calculated stress.

http://www.osti.gov/bridge/purl.cov...A94E1C94ACE4?purl=/469147-jdOZBI/webviewable/

In your case, it sounds like you have ordered pairs [itex](G_i,t_i)[/itex] and want to fit the data to a function that looks something like:

[tex]G(t) = G_0 + \sum_{i=1}^n G_i e^{t/\tau_i}[/tex]

where [itex]G_0[/itex] is known and you want to obtain estimates for the [tex]G_i[/itex] and [itex]\tau_i[/itex]. Since the number of unknowns is 2*n, you need at least 2*n data points.
 
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1. What is a Prony series and how is it used in fitting data?

A Prony series is a mathematical model used to represent a signal or data set. It is a series of decaying exponential functions that can be used to approximate a wide range of signals. In fitting data, the Prony series is used to find the best-fit parameters that can accurately represent the data.

2. How is the goodness of fit determined when using a Prony series?

The goodness of fit for a Prony series is typically determined by calculating the root-mean-square error (RMSE) between the fitted series and the original data. A lower RMSE value indicates a better fit.

3. Can a Prony series be used to fit non-exponential data?

Yes, a Prony series can be used to fit non-exponential data. It is a flexible model that can be adjusted to fit a variety of data sets, including non-exponential ones.

4. What are some common challenges when fitting data to a Prony series?

One common challenge is choosing the appropriate number of terms in the series. Too few terms may result in an inadequate fit, while too many terms can lead to overfitting. Another challenge is dealing with noisy data, as the Prony series is sensitive to outliers.

5. Are there any limitations to using a Prony series for data fitting?

While the Prony series is a versatile model, it may not be suitable for all types of data. For example, it may not be effective for highly oscillatory data or data with sharp transitions. It is important to carefully evaluate the data and consider other fitting methods if necessary.

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