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预求出系数k0k1k2k3.同时计算出r的模拟值和实验值的相对误差,谢谢您了rA=[0.0085540.0085630.0085700.0085730.0085750.0085640.0085930.0085460.0085330.0085670.0085650.0085250.0085030.0085390.0085460.0084860.00
题目详情
预求出系数k0 k1 k2 k3 .同时计算出r的模拟值和实验值的相对误差,谢谢您了
rA = [0.008554 0.008563 0.008570 0.008573 0.008575 0.008564 0.008593 0.008546 0.008533 0.008567 0.008565 0.008525 0.008503 0.008539 0.008546 0.008486 0.008476 0.008476 0.008477]; x = [5.705 5.988 6.211 6.383 6.535 6.708 6.779 6.961 7.032 7.103 7.164 7.306 7.417 7.417 7.488 7.589 7.630 7.691 7.731]; y = [22.778 22.869 22.950 22.960 22.960 22.991 23.092 22.899 22.697 22.950 22.910 22.737 22.707 22.879 22.960 22.626 22.525 22.585 22.595 z = [0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689]; 已知函数的形式是r=(k1*x-k3*y)*k0*z/((k1*x+k2*x+ k3*y+ k0*z)
rA = [0.008554 0.008563 0.008570 0.008573 0.008575 0.008564 0.008593 0.008546 0.008533 0.008567 0.008565 0.008525 0.008503 0.008539 0.008546 0.008486 0.008476 0.008476 0.008477]; x = [5.705 5.988 6.211 6.383 6.535 6.708 6.779 6.961 7.032 7.103 7.164 7.306 7.417 7.417 7.488 7.589 7.630 7.691 7.731]; y = [22.778 22.869 22.950 22.960 22.960 22.991 23.092 22.899 22.697 22.950 22.910 22.737 22.707 22.879 22.960 22.626 22.525 22.585 22.595 z = [0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689]; 已知函数的形式是r=(k1*x-k3*y)*k0*z/((k1*x+k2*x+ k3*y+ k0*z)
▼优质解答
答案和解析
rA = [0.008554 0.008563 0.008570 0.008573 0.008575 0.008564 0.008593 0.008546 0.008533 0.008567 0.008565 0.008525 0.008503 0.008539 0.008546 0.008486 0.008476 0.008476 0.008477];
x = [5.705 5.988 6.211 6.383 6.535 6.708 6.779 6.961 7.032 7.103 7.164 7.306 7.417 7.417 7.488 7.589 7.630 7.691 7.731];
y = [22.778 22.869 22.950 22.960 22.960 22.991 23.092 22.899 22.697 22.950 22.910 22.737 22.707 22.879 22.960 22.626 22.525 22.585 22.595];
z = [0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689];
f=@(k,x) (k(1)*x(1,:)-k(3)*x(2,:)).*k(4).*x(3,:)./(k(1)*x(1,:)+k(2)*x(1,:)+ k(3)*x(2,:)+ k(4)*x(3,:))
[k,res]=lsqcurvefit(f,[1 1 1 1],[x; y; z],rA)
res就是误差了
x = [5.705 5.988 6.211 6.383 6.535 6.708 6.779 6.961 7.032 7.103 7.164 7.306 7.417 7.417 7.488 7.589 7.630 7.691 7.731];
y = [22.778 22.869 22.950 22.960 22.960 22.991 23.092 22.899 22.697 22.950 22.910 22.737 22.707 22.879 22.960 22.626 22.525 22.585 22.595];
z = [0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689 0.689];
f=@(k,x) (k(1)*x(1,:)-k(3)*x(2,:)).*k(4).*x(3,:)./(k(1)*x(1,:)+k(2)*x(1,:)+ k(3)*x(2,:)+ k(4)*x(3,:))
[k,res]=lsqcurvefit(f,[1 1 1 1],[x; y; z],rA)
res就是误差了
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