MATLAB实例：多元函数拟合(线性与非线性)

做者：凯鲁嘎吉 - 博客园 http://www.cnblogs.com/kailugaji/html

如今用最小二乘法拟合多元函数，实现线性拟合与非线性拟合，其中非线性拟合要求自定义拟合函数。3d

下面给出三种拟合方式，第一种是多元线性拟合(回归)，第二三种是多元非线性拟合，实际中第二三种方法是一个意思，任选一种便可，推荐第二种拟合方法。code

1. MATLAB程序

fit_nonlinear_data.m

function [beta, r]=fit_nonlinear_data(X, Y, choose) % Input: X 自变量数据(N, D)， Y 因变量(N, 1)，choose 1-regress, 2-nlinfit 3-lsqcurvefit if choose==1 X1=[ones(length(X(:, 1)), 1), X]; [beta, bint, r, rint, states]=regress(Y, X1) % 多元线性回归 % y=beta(1)+beta(2)*x1+beta(3)*x2+beta(4)*x3+... % beta—系数估计 % bint—系数估计的上下置信界 % r—残差 % rint—诊断异常值的区间 % states—模型统计信息 rcoplot(r, rint) saveas(gcf,sprintf('线性曲线拟合_残差图.jpg'),'bmp'); elseif choose==2 beta0=ones(7, 1); % 初始值的选取可能会致使结果具备较大的偏差。 [beta, r, J]=nlinfit(X, Y, @myfun, beta0) % 非线性回归 % beta—系数估计 % r—残差 % J—雅可比矩阵 [Ypred,delta]=nlpredci(@myfun, X, beta, r, 'Jacobian', J) % 非线性回归预测置信区间 % Ypred—预测响应 % delta—置信区间半角 plot(X(:, 1), Y, 'k.', X(:, 1), Ypred, 'r'); saveas(gcf,sprintf('非线性曲线拟合_1.jpg'),'bmp'); elseif choose==3 beta0=ones(7, 1); % 初始值的选取可能会致使结果具备较大的偏差。 [beta,resnorm,r, ~, ~, ~, J]=lsqcurvefit(@myfun,beta0,X,Y) % 在最小二乘意义上解决非线性曲线拟合（数据拟合）问题 % beta—系数估计 % resnorm—残差的平方范数 sum((fun(x,xdata)-ydata).^2) % r—残差 r=fun(x,xdata)-ydata % J—雅可比矩阵 [Ypred,delta]=nlpredci(@myfun, X, beta, r, 'Jacobian', J) plot(X(:, 1), Y, 'k.', X(:, 1), Ypred, 'r'); saveas(gcf,sprintf('非线性曲线拟合_2.jpg'),'bmp'); end end function yy=myfun(beta,x) %自定义拟合函数 yy=beta(1)+beta(2)*x(:, 1)+beta(3)*x(:, 2)+beta(4)*x(:, 3)+beta(5)*(x(:, 1).^2)+beta(6)*(x(:, 2).^2)+beta(7)*(x(:, 3).^2); end

demo.m

clear clc X=[1 13 1.5; 1.4 19 3; 1.8 25 1; 2.2 10 2.5;2.6 16 0.5; 3 22 2; 3.4 28 3.5; 3.5 30 3.7]; Y=[0.330; 0.336; 0.294; 0.476; 0.209; 0.451; 0.482; 0.5]; choose=1; fit_nonlinear_data(X, Y, choose)

2. 结果

(1)多元线性拟合(regress)

choose=1:orm

>> demo beta = 0.200908829282537 0.044949392540298 -0.003878606875016 0.070813489681112 bint = -0.026479907290565 0.428297565855639 -0.057656451966002 0.147555237046598 -0.017251051845827 0.009493838095795 0.000201918738160 0.141425060624065 r = 0.028343433030705 -0.066584917256987 0.038333946339215 0.037954851676187 -0.082126284727611 0.058945364984698 -0.010982985302994 -0.003883408743214 rint = -0.151352966773048 0.208039832834458 -0.188622801533810 0.055452967019837 -0.090283529625345 0.166951422303776 -0.090266067743345 0.166175771095720 -0.108068661106325 -0.056183908348897 -0.130409602930181 0.248300332899576 -0.206254481234707 0.184288510628719 -0.184329400080620 0.176562582594191 states = 0.768591079367914 4.428472778943478 0.092289917768436 0.004625488283939

(2)多元非线性拟合(nlinfit)

choose=2:htm

>> demo beta = 0.312525876099987 0.015300533415459 -0.036942272680920 0.299760796634952 0.009412595106141 0.000976411370591 -0.062931846673372 r = 1.0e-03 * -0.047521336834000 0.127597019984715 -0.092883949615763 -0.040370056416994 0.031209476614974 0.211856736183458 -0.727835090583939 0.537947200592082 J = 1.0e+02 * 0.010000000000266 0.010000000001236 0.129999999998477 0.014999999999641 0.010000000007909 1.689999999969476 0.022499999999756 0.010000000000266 0.014000000006524 0.189999999999301 0.029999999999283 0.019600000006932 3.609999999769248 0.089999999999024 0.010000000000266 0.018000000011811 0.249999999990199 0.009999999999965 0.032399999999135 6.250000000033778 0.010000000000377 0.009999999999679 0.022000000005116 0.099999999998065 0.025000000000218 0.048400000003999 1.000000000103046 0.062500000001264 0.009999999999972 0.025999999998421 0.159999999998889 0.004999999999982 0.067599999997174 2.559999999999039 0.002499999999730 0.009999999999679 0.029999999991726 0.219999999999713 0.019999999999930 0.089999999993269 4.839999999890361 0.040000000000052 0.009999999999092 0.033999999985031 0.279999999990611 0.034999999998348 0.115599999997155 7.839999999636182 0.122500000000614 0.010000000000266 0.034999999992344 0.299999999994194 0.037000000000420 0.122499999994626 8.999999999988553 0.136899999999292 Ypred = 0.330047521336834 0.335872402980015 0.294092883949616 0.476040370056417 0.208968790523385 0.450788143263817 0.482727835090584 0.499462052799408 delta = 0.011997285626178 0.011902559677366 0.011954353934643 0.012001513980794 0.012005923574387 0.011706970437467 0.007666390995581 0.009878186927507

(3)多元非线性拟合(lsqcurvefit)

choose=3:blog

>> demo beta = 0.312525876070457 0.015300533464733 -0.036942272680581 0.299760796608728 0.009412595094407 0.000976411370579 -0.062931846666179 resnorm = 8.937848643213721e-07 r = 1.0e-03 * 0.047521324135769 -0.127597015215197 0.092883952947764 0.040370060121864 -0.031209466218374 -0.211856745335304 0.727835089662676 -0.537947200236699 J = 1.0e+02 * (1,1) 0.010000000000000 (2,1) 0.010000000000000 (3,1) 0.010000000000000 (4,1) 0.010000000000000 (5,1) 0.010000000000000 (6,1) 0.010000000000000 (7,1) 0.010000000000000 (8,1) 0.010000000000000 (1,2) 0.010000000000000 (2,2) 0.014000000059605 (3,2) 0.017999999970198 (4,2) 0.022000000029802 (5,2) 0.026000000014901 (6,2) 0.030000000000000 (7,2) 0.034000000059605 (8,2) 0.035000000000000 (1,3) 0.130000000000000 (2,3) 0.190000000000000 (3,3) 0.250000000000000 (4,3) 0.100000000000000 (5,3) 0.160000000000000 (6,3) 0.220000000000000 (7,3) 0.280000000000000 (8,3) 0.300000000000000 (1,4) 0.015000000000000 (2,4) 0.030000000000000 (3,4) 0.010000000000000 (4,4) 0.025000000000000 (5,4) 0.005000000000000 (6,4) 0.020000000000000 (7,4) 0.035000000000000 (8,4) 0.036999999880791 (1,5) 0.010000000000000 (2,5) 0.019599999934435 (3,5) 0.032399999983609 (4,5) 0.048400000035763 (5,5) 0.067599999997765 (6,5) 0.090000000000000 (7,5) 0.115600000023842 (8,5) 0.122500000000000 (1,6) 1.690000000000000 (2,6) 3.610000000000000 (3,6) 6.250000000000000 (4,6) 1.000000000000000 (5,6) 2.560000000000000 (6,6) 4.840000000000000 (7,6) 7.840000000000000 (8,6) 9.000000000000000 (1,7) 0.022500000000000 (2,7) 0.090000000000000 (3,7) 0.010000000000000 (4,7) 0.062500000000000 (5,7) 0.002500000000000 (6,7) 0.040000000000000 (7,7) 0.122500000000000 (8,7) 0.136899999976158 Ypred = 0.330047521324136 0.335872402984785 0.294092883952948 0.476040370060122 0.208968790533782 0.450788143254665 0.482727835089663 0.499462052799763 delta = 0.011997285618724 0.011902559623756 0.011954353977139 0.012001513949620 0.012005923574975 0.011706970418735 0.007666391016173 0.009878186931566

注意：多元非线性函数拟合中参数的初始值须要提早设置，有些状况下，参数的初始选取对函数拟合结果影响极大，须要谨慎处理。第二三种方法中，因为数据是多维的，所以只展现了第一个维度的拟合函数图。若有须要，可自行修改。ci