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Nonlinear least square regression

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ameen 2013-5-27

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https://ww2.mathworks.cn/matlabcentral/answers/77106-nonlinear-least-square-regression

i have (x , y) data

the function between x and y is y = 0.392* (1 - (x / b1) .^ b2

i want to use nonlinear least square regression to obtain the values of b1 and b2

can any one help me with the structure of the Matlab program

thanks in advance

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Matt J 2013-5-27

编辑：Matt J 2013-5-27

You're missing a parenthesis ')' in your regression model. It's important for us to know where it should go.

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回答（1 个）

the cyclist 2013-5-27

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https://ww2.mathworks.cn/matlabcentral/answers/77106-nonlinear-least-square-regression#answer_86773

在 MATLAB Online 中打开

If you have the Statistics Toolbox, then you can use the nlinfit() function.

Type

doc nlinfit

in the interface, or see this page for details: http://www.mathworks.com/help/stats/nlinfit.html

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ameen 2013-5-27

编辑：ameen 2013-5-27

在 MATLAB Online 中打开

Using nlinfit:

data=[0.5040371 0.261115

4485177 0.246058
40458907 0.248194
29249153 0.272286
502260793 0.242385
448165094 0.226992
402305 0.232877
288641 0.266967
501377 0.275141
447111 0.272572
397536 0.272892
335926 0.275011
500058 0.249249
44641 0.24623
403158 0.252487
341357 0.265364
0.39185];
t = data(:,1);
y = data(:,2);

F = @(x,xdata)0.392*(1-(xdata/x(1))).^x(2);

x0 = [0.65 1];

beta = nlinfit(t,y,F,x0)

beta =

   1.0e+09 *
    1.9793    2.0014

Using lsqcurvefit :

data=[0.5040371 0.261115

4485177 0.246058
40458907 0.248194
29249153 0.272286
502260793 0.242385
448165094 0.226992
402305 0.232877
288641 0.266967
501377 0.275141
447111 0.272572
397536 0.272892
335926 0.275011
500058 0.249249
44641 0.24623
403158 0.252487
341357 0.265364
0.39185];
t = data(:,1);
y = data(:,2);

F = @(x,xdata)0.392*(1-(xdata/x(1))).^x(2);

x0 = [0.65 1];

[x,resnorm,~,exitflag,output] = lsqcurvefit(F,x0,t,y);

x =

258.1339 261.1441

which one is correct ???

ameen 2013-5-27

Thank you very much Matt

so the problem is with my data ??

Matt J 2013-5-27

编辑：Matt J 2013-5-27

在 MATLAB Online 中打开

Or your model. Maybe you should be fitting

y=x(1)*exp(-x(2)*t)

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