帮我求下用最小二乘法拟合一个多项式我自己用MATLAB拟合一个5次多项式的系数如:y=a+bx+cx^2+dx^3+ex^4+fx^5,无论用最小二乘法命令lspoly()还是ployfit()算的结果都有警告,我自己检查了一下我算
来源:学生作业帮助网 编辑:作业帮 时间:2024/06/28 19:59:59
![帮我求下用最小二乘法拟合一个多项式我自己用MATLAB拟合一个5次多项式的系数如:y=a+bx+cx^2+dx^3+ex^4+fx^5,无论用最小二乘法命令lspoly()还是ployfit()算的结果都有警告,我自己检查了一下我算](/uploads/image/z/10066204-28-4.jpg?t=%E5%B8%AE%E6%88%91%E6%B1%82%E4%B8%8B%E7%94%A8%E6%9C%80%E5%B0%8F%E4%BA%8C%E4%B9%98%E6%B3%95%E6%8B%9F%E5%90%88%E4%B8%80%E4%B8%AA%E5%A4%9A%E9%A1%B9%E5%BC%8F%E6%88%91%E8%87%AA%E5%B7%B1%E7%94%A8MATLAB%E6%8B%9F%E5%90%88%E4%B8%80%E4%B8%AA5%E6%AC%A1%E5%A4%9A%E9%A1%B9%E5%BC%8F%E7%9A%84%E7%B3%BB%E6%95%B0%E5%A6%82%EF%BC%9Ay%3Da%2Bbx%2Bcx%5E2%2Bdx%5E3%2Bex%5E4%2Bfx%5E5%2C%E6%97%A0%E8%AE%BA%E7%94%A8%E6%9C%80%E5%B0%8F%E4%BA%8C%E4%B9%98%E6%B3%95%E5%91%BD%E4%BB%A4lspoly%28%29%E8%BF%98%E6%98%AFployfit%EF%BC%88%EF%BC%89%E7%AE%97%E7%9A%84%E7%BB%93%E6%9E%9C%E9%83%BD%E6%9C%89%E8%AD%A6%E5%91%8A%2C%E6%88%91%E8%87%AA%E5%B7%B1%E6%A3%80%E6%9F%A5%E4%BA%86%E4%B8%80%E4%B8%8B%E6%88%91%E7%AE%97)
帮我求下用最小二乘法拟合一个多项式我自己用MATLAB拟合一个5次多项式的系数如:y=a+bx+cx^2+dx^3+ex^4+fx^5,无论用最小二乘法命令lspoly()还是ployfit()算的结果都有警告,我自己检查了一下我算
帮我求下用最小二乘法拟合一个多项式
我自己用MATLAB拟合一个5次多项式的系数如:y=a+bx+cx^2+dx^3+ex^4+fx^5,无论用最小二乘法命令lspoly()还是ployfit()算的结果都有警告,我自己检查了一下我算的结果不对;
如题:>> x=[1000 1250 1500 1750 2000 2250 2500 2750 3000];
>> y=[300 312 310 303 298 292 288 280 272];
>> f=lspoly(x,y,5)
Warning:Matrix is close to singular or badly scaled.
Results may be inaccurate.RCOND = 3.779465e-040.
> In lspoly at 11
c =
0.0000
-0.0000
0.0000
-0.0018
1.8802
-431.8485
帮我用MATLAB把这个系数拟合出来吧,我算的这个结果应该是错的.本人现在百度还没什么财富,以后有机会再加啊,急用啊
帮我求下用最小二乘法拟合一个多项式我自己用MATLAB拟合一个5次多项式的系数如:y=a+bx+cx^2+dx^3+ex^4+fx^5,无论用最小二乘法命令lspoly()还是ployfit()算的结果都有警告,我自己检查了一下我算
Warnings during fitting:
Equation is badly conditioned.Remove repeated data points
or try centering and scaling.
这是因为你的数据点不好.
此外,你用的拟合函数的阶数很高……如果换成二阶就可以使用polyfit()了.
下面是用curve fitting tool的拟合结果:
Linear model Poly5:
f(x) = p1*x^5 + p2*x^4 + p3*x^3 + p4*x^2 + p5*x + p6
Coefficients (with 95% confidence bounds):
p1 = 1.739e-14 (2.242e-15,3.255e-14)
p2 = -1.951e-10 (-3.468e-10,-4.337e-11)
p3 = 8.54e-07 (2.646e-07,1.443e-06)
p4 = -0.001825 (-0.002932,-0.0007169)
p5 = 1.88 (0.8761,2.884)
p6 = -431.8 (-781.9,-81.76)
Goodness of fit:
SSE:1.349
R-square:0.9991
Adjusted R-square:0.9975
RMSE:0.6706