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  • Matlab:拟合(2)

    非线性最小二乘拟合:

    解法一:用命令lsqcurvefit

    1 function f = curvefun(x, tdata)
    2 f = x(1) + x(2)*exp(0.02 * x(3) * tdata);
    3 %其中x(1) = a; x(2) = b; x(3) = c;
     1 %数据输入
     2 tdata = 100:100:1000;
     3 cdata = 1e-03 * [4.54, 4.99, 5.35, 5.65, 5.90, 6.10, 6.26, 6.39, 6.50, 6.59];
     4 %设定预测值
     5 x0 = [0.2 0.05 0.05];
     6 %非线性拟合函数
     7 x = lsqcurvefit('curvefun', x0, tdata, cdata)
     8 %作图
     9 f = curvefun(x, tdata)
    10 plot(tdata, cdata, 'k+')
    11 hold on
    12 plot(tdata, f, 'r')

    结果:
    x =

       -0.0074    0.0116    0.0118

    f =

      Columns 1 through 8

        0.0044    0.0047    0.0050    0.0053    0.0056    0.0059    0.0062    0.0066

      Columns 9 through 10

        0.0069    0.0072

    解法二:用命令lsqnonlin

    1 function f = curvefun1(x)
    2 %curvefun1的自变量是x,cdata和tdata是已知参数,故应将cdata,tdata的值卸载curvefun1中
    3 tdata = 100:100:1000;
    4 cdata = 1e-03 * [4.54, 4.99, 5.35, 5.65, 5.90, 6.10, 6.26, 6.39, 6.50, 6.59];
    5 f = x(1) + x(2)*exp(0.02 * x(3) * tdata) - cdata;%注意
    1 tdata = 100:100:1000;
    2 cdata = 1e-03 * [4.54, 4.99, 5.35, 5.65, 5.90, 6.10, 6.26, 6.39, 6.50, 6.59];
    3 %预测值
    4 x0 = [0.2 0.05 0.05];
    5 x = lsqnonlin('curvefun1', x0)
    6 f = curvefun1(x)
    7 plot(tdata, cdata, 'k+')
    8 hold on
    9 plot(tdata, f+cdata, 'r')

    结果:

    x =

       -0.0074    0.0116    0.0118

    f =

      1.0e-003 *

      Columns 1 through 8

       -0.1168   -0.2835   -0.3534   -0.3564   -0.3022   -0.1908   -0.0320    0.1645

      Columns 9 through 10

        0.3888    0.6411

    ——现在的努力是为了小时候吹过的牛B!!
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  • 原文地址:https://www.cnblogs.com/pingge/p/3261916.html
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