So I'm trying to identify a system that happens to be a synchronus generator via linear regression. I've got a model with the(adsbygoogle = window.adsbygoogle || []).push({}); unknown coefficients A, B and C,and themeasuredvariables I, w and Taccording to

I(w, T) = A*T + B*w + C

1.What I fear is that I could get multiple solutions that all are very similar in their error estimates. But, due to measurement errors, the one that shows the smallest error isn't the most accurate estimation in reality. Am I thinking correctly here?

2.I do have the possibility to run a number of tests with a fixed T, only varing W. Thus I can create a an approximate partial derivate of the function so

∂ I/∂w = B

Then I can have the value for B fixed, when searching for the values for A and C in a mesurement series with a varying T. Would this statistically decrease the risk for what is describe in (1)? I cannot get A with the same method, as I can't lock the value for w.

Any litterature and theory tips would be great so that I can learn more.

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# Linear regression and measured values

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