Fitting problem
Least squares fitting
Linear fit
Polynomial fitting
Nonlinear fitting that can be transformed into linear fitting
Polynomial curve fitting function
polyfit（）
Call format
p=polyfit(x,y,n)
[p,s]=polyfit(x,y,n)
Explanation: x and y are the data points, n is the polynomial order, and return p is the polynomial coefficient vector p with power from high to bottom. The matrix s is used to generate an error estimate of the predicted value.
Case
| x | 0 | .1 | .2 | .3 | .4 | .5 | .6 | .7 | .8 | .9 | 10 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| and | .3 | .5 | 1 | 1.4 | 1.6 | 1.9 | .6 | .4 | .8 | 1.5 | 2 |
Fit a polynomial
x=0:.1:1;
y=[.3 .1 1 1.4 1.6 1.9 .6 .4 .8 1.5 2]
n=3;
p=polyfit(x,y,n)
xi=linspace(0,1,100);
z=polyval(p,xi);
plot(x,y,'o',xi,z,'k',x,y,'b')
z=polyval(p,xi);
plot(x,y,‘o’,xi,z,‘k’,x,y,‘b’)
