神马作文网matlab 曲线拟合的问题
来源：学生作业帮 编辑：神马作文网作业帮 分类：综合作业 时间：2024/09/22 06:45:36
matlab 曲线拟合的问题
x=[1.870592E+07
2.003366E+07
2.074818E+07
2.194041E+07
2.264339E+07
2.330242E+07
2.321209E+07
2.324987E+07
1.414927E+07
1.413807E+07
1.879982E+07
1.901068E+07
1.928789E+07
1.949394E+07
1.968775E+07
2.003045E+07
2.037739E+07
2.060624E+07
2.067867E+07
2.121926E+07
2.161368E+07
2.196016E+07
2.235986E+07
2.266084E+07
2.303457E+07
2.300120E+07
2.359826E+07
2.322719E+07
1.431619E+07
1.420434E+07
1.419769E+07
];
y=[-20.00
-17.00
-15.00
-12.00
-10.00
-8.00
-7.00
-6.00
-5.50
-5.30
-5.00
-4.50
-4.00
-3.50
-3.00
-2.00
-1.00
-0.50
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
9.50
9.60
9.80
];
plot(y,x)
p=polyfit(y,x,9);
hold on
plot(y,p(1)*y.^9+p(2).*y.^8+p(3).*y.^7+p(4).*y.^6+p(5).*y.^5+p(6).*y.^4+p(7).*y.^3+p(8).*y.^2+p(9).*y.^1+p(10))
画出的图,还是不太一致啊
x=[1.870592E+07
2.003366E+07
2.074818E+07
2.194041E+07
2.264339E+07
2.330242E+07
2.321209E+07
2.324987E+07
1.414927E+07
1.413807E+07
1.879982E+07
1.901068E+07
1.928789E+07
1.949394E+07
1.968775E+07
2.003045E+07
2.037739E+07
2.060624E+07
2.067867E+07
2.121926E+07
2.161368E+07
2.196016E+07
2.235986E+07
2.266084E+07
2.303457E+07
2.300120E+07
2.359826E+07
2.322719E+07
1.431619E+07
1.420434E+07
1.419769E+07
];
y=[-20.00
-17.00
-15.00
-12.00
-10.00
-8.00
-7.00
-6.00
-5.50
-5.30
-5.00
-4.50
-4.00
-3.50
-3.00
-2.00
-1.00
-0.50
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
9.50
9.60
9.80
];
plot(y,x)
p=polyfit(y,x,9);
hold on
plot(y,p(1)*y.^9+p(2).*y.^8+p(3).*y.^7+p(4).*y.^6+p(5).*y.^5+p(6).*y.^4+p(7).*y.^3+p(8).*y.^2+p(9).*y.^1+p(10))
画出的图,还是不太一致啊
可以这样写
plot(y,x,'.-')
p=polyfit(y,x,9);
hold on
xv=polyval(p,y);
plot(y,xv,'r-*')
至于图形的问题 可能你选的公式不太好
再问： 选什么公式好呢 ？
再答： Fit found when optimization terminated: General model Gauss5: f(x) = a1*exp(-((x-b1)/c1)^2) + a2*exp(-((x-b2)/c2)^2) + a3*exp(-((x-b3)/c3)^2) + a4*exp(-((x-b4)/c4)^2) + a5*exp(-((x-b5)/c5)^2) Coefficients (with 95% confidence bounds): a1 = 2.643e+009 (-4.145e+016, 4.145e+016) b1 = 8.513 (-3.23e+004, 3.232e+004) c1 = 0.1987 (-2.462e+005, 2.462e+005) a2 = 4.913e+006 (-1.937e+006, 1.176e+007) b2 = 1.375 (-1.603, 4.353) c2 = 2.897 (-0.05885, 5.853) a3 = 2.285e+007 (2.182e+007, 2.388e+007) b3 = -10.72 (-11.93, -9.501) c3 = 18.58 (13.95, 23.21) a4 = 1.33e+007 (9.172e+006, 1.743e+007) b4 = 6.752 (5.029, 8.475) c4 = 3.76 (1.864, 5.655) a5 = -7.737e+006 (-9.692e+006, -5.782e+006) b5 = -5.383 (-5.469, -5.297) c5 = 0.3083 (0.161, 0.4556) Goodness of fit: SSE: 1.373e+013 R-square: 0.9507 Adjusted R-square: 0.9075 RMSE: 9.263e+005
再问： 一开始用的是多项式公式对吗？现在用的是对数。 xv=polyval(p,y); 这句是什么意思
再答： 现在是用的指数 xv=polyval(p,y);表示将y的值带入以p为系数的多项式求出函数值
plot(y,x,'.-')
p=polyfit(y,x,9);
hold on
xv=polyval(p,y);
plot(y,xv,'r-*')
至于图形的问题 可能你选的公式不太好
再问： 选什么公式好呢 ？
再答： Fit found when optimization terminated: General model Gauss5: f(x) = a1*exp(-((x-b1)/c1)^2) + a2*exp(-((x-b2)/c2)^2) + a3*exp(-((x-b3)/c3)^2) + a4*exp(-((x-b4)/c4)^2) + a5*exp(-((x-b5)/c5)^2) Coefficients (with 95% confidence bounds): a1 = 2.643e+009 (-4.145e+016, 4.145e+016) b1 = 8.513 (-3.23e+004, 3.232e+004) c1 = 0.1987 (-2.462e+005, 2.462e+005) a2 = 4.913e+006 (-1.937e+006, 1.176e+007) b2 = 1.375 (-1.603, 4.353) c2 = 2.897 (-0.05885, 5.853) a3 = 2.285e+007 (2.182e+007, 2.388e+007) b3 = -10.72 (-11.93, -9.501) c3 = 18.58 (13.95, 23.21) a4 = 1.33e+007 (9.172e+006, 1.743e+007) b4 = 6.752 (5.029, 8.475) c4 = 3.76 (1.864, 5.655) a5 = -7.737e+006 (-9.692e+006, -5.782e+006) b5 = -5.383 (-5.469, -5.297) c5 = 0.3083 (0.161, 0.4556) Goodness of fit: SSE: 1.373e+013 R-square: 0.9507 Adjusted R-square: 0.9075 RMSE: 9.263e+005
再问： 一开始用的是多项式公式对吗？现在用的是对数。 xv=polyval(p,y); 这句是什么意思
再答： 现在是用的指数 xv=polyval(p,y);表示将y的值带入以p为系数的多项式求出函数值