matlab非线性回归命令流

1、x=13855,15937,13860,14361,17996,24304,27597,31976,70990,105874,108427,113163 ,116634,93218,82989,135975,135809,139319,149489,166817,167160,168315,162228,1 58314;y=15248,15574,16055,15381,18329,19928,20108,20654,21011,21920,23344,26100,29 438,32430,37620,60335,70620,86700,102324,120636,136725,135277,

2、131266,112731; 24年的数据函数是 y=a*x+b*x2 另一函数是 y=x*exp(c+d*x;a b c d是参数请各位大牛帮我用 matlab 进行非线形回归 , 解出这些参数 ,20分送上 问题补充:谢谢您的解答 . 我有些地方不是很明白 . 望解答因为我们捕捞这个专业的剩余产量模式 ,y 不能随着 x 增长而无限增长 , 当 x 太大而使资源受 到破坏 ,y 会减少 . 所以模式的 b 和 d 应该为负的 , 而 a 和 c 为正是不是给的数据有问题 ? 还是这个公式改成 y=a*x-b*x2 ?这个我想不明白 谢谢您的无私 【补充】x 与 y 互换,还能成立不?clc

3、;clearx=13855,15937,13860,14361,17996,24304,27597,31976,70990,105874,108427,113163 ,116634,93218,82989,135975,135809,139319,149489,166817,167160,168315,162228,1 58314;y=15248,15574,16055,15381,18329,19928,20108,20654,21011,21920,23344,26100,29 438,32430,37620,60335,70620,86700,102324,120636,136725,1

4、35277,131266,112731; fun1=inline('a(1*x+a(2*x.2','a','x'fun2=inline('x.*exp(b(1+b(2*x','b','x'format short ga=nlinfit(x,y,fun1,-0.1 1e-006b=nlinfit(x,y,fun2,-2 1e-005xx=min(x:100:max(x;y1=a(1*xx+a(2*xx.2;y2=xx.*exp(b(1+b(2*xx;plot(x,y,'o',xx,y1

5、,xx,y2结果:fun1 =Inline function:fun1(a,x = a(1*x+a(2*x.2fun2 =Inline function:fun2(b,x = x.*exp(b(1+b(2*xa =-0.18297 5.5155e-006b =-2.6657 1.4614e-0052 clc;clear;x=2 3 4 5 6 7 8 9;y=0.00374 0.21048 0.4116 0.19831 0.1001 0.03555 0.01496 0.01216;beta0=2 2 2 2 2;betafit=nlinfit(x,y,modle,beta0;b=beta(1;

6、a=beta(2;u=beta(3;c=beta(4;w=beta(5;function y1=modle(beta,xb=beta(1;a=beta(2;u=beta(3;c=beta(4;w=beta(5;y1=b*(1/(x*a*(2*3.14159260.5*exp(-(log(x-u/(2*a+(1-b*c*exp(-c*(x-w-exp(-c*(x-w;clc;clear;x=2 3 4 5 6 7 8 9;y=0.00374 0.21048 0.4116 0.19831 0.1001 0.03555 0.01496 0.01216;myfunc=inline('beta(

7、1*(1/(x*beta(2*(2*3.14159260.5*exp(-0.5*(log(x-beta(3/beta(2+(1 -beta(1*beta(4*exp(-beta(4*(x-beta(5-exp(-beta(4*(x-beta(5','beta','x'beta=nlinfit(x,y,myfunc,5 5 5 5 5;b=beta(1a=beta(2u=beta(3c=beta(4w=beta(5clc;clear;x=2 3 4 5 6 7 8 9 ;y=0.00374 0.21048 0.4116 0.19831 0.1001 0.03555 0.01496 0.01216 ;beta0=1 1 1 1 1;beta=nlinfit(x,y,myfunc,beta0;b=beta(1;a=beta(2;u=beta(3;c=beta(4;w=beta(5;function g=myfunc(beta,xg=beta(1*(1