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How do I apply exponential and logarithmic curve fitting

조회 수: 89 (최근 30일)
Iro
Iro 2014년 2월 19일
댓글: yeungor 2016년 9월 25일
Hi,
I have some scatterplots and I want to check the relationships between the variables which resemble exponential and logarithmic functions. I have tried to use the functions
nlinfit
fittype
fit
but so far not successfully (poor fitting or code not working at all).
How can I check the curves for the above scatters which seem to have the following functions:
y= a*exp(b*x)+c and y=log_a(x)+b
in matlab?
Thanks
Iro
  댓글 수: 3
Iro
Iro 2014년 2월 20일
편집: Iro 2014년 2월 20일
I mean that I make some kind of mistake in the definition of the function when I use nlinfit so the code does not work.
With log_a(x) I mean the logarithm of x with base a.
yeungor
yeungor 2016년 9월 25일
Have you tried fitting an exponential of the inverse? If x = f(y), then y = f^-1(x) and you can use 'fit' and the 'exp' for an exponential fit.

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채택된 답변

Star Strider
Star Strider 2014년 2월 22일
The nlinfit function requires that the first argument of the objective function is the parameter vector and the second the vector of independent variables. Using anonymous functions,
y = a*exp(b*x)+c
becomes
y = @(B,x) B(1).*exp(B(2).*x) + B(3); % B(1) = a, B(2) = b, B(3) = c
For the logarithmic fit, all logs to various bases are simply scaled by a constant. Consider:
a^y = b^x
Taking the log to base a (denoted by loga()) of both sides gives:
y = x*loga(b)
so the log to any base will work.
The anonymous function for your logarithmic regression is then:
y = @(B,x) log(x) + B; % B = b
or alternatively,
y = @(B,x) B(1).*log(x) + B(2);
Those should work with nlinfit.

추가 답변 (1개)

Danilo NASCIMENTO
Danilo NASCIMENTO 2014년 2월 19일
You can use cf = fit(x,y,'exp1'); where x and y are your set of points.
  댓글 수: 1
Iro
Iro 2014년 2월 20일
Hi, thanks for your answer. I have used this function for the exxponential scatter but it doesn't give me good fitting. Do you know how I can get into account the constant c in the above equation using fit with 'exp1' ?
Thanks

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