Matlab自带的曲线拟合程序

这个函数的功能是能自动搜索参数的取值，从而使得方程的误差最小。

效果如下

代码如下

%% Optimal Fit of a Non-linear Function

% This is a demonstration of the optimal fitting of a non-linear function to a

% set of data.  It uses FMINSEARCH, an implementation of the Nelder-Mead simplex

% (direct search) algorithm, to minimize a nonlinear function of several

% variables.

%

% Copyright 1984-2002 The MathWorks, Inc.

% $Revision: 5.15 $ $Date: 2002/04/02 17:52:33 $

%%

% First, create some sample data and plot it.

% t = (0:.1:2)';

% y = [5.8955 3.5639 2.5173 1.9790 1.8990 1.3938 1.1359 1.0096 1.0343 ...

%      0.8435 0.6856 0.6100 0.5392 0.3946 0.3903 0.5474 0.3459 0.1370 ...

%      0.2211 0.1704 0.2636]';

function [coeff,estimated_lambda] = fitdemo(XData, YData)

if nargin<1,

    XData = (0:.1:2)';

    YData = [5.8955 3.5639 2.5173 1.9790 1.8990 1.3938 1.1359 1.0096 1.0343 ...

        0.8435 0.6856 0.6100 0.5392 0.3946 0.3903 0.5474 0.3459 0.1370 ...

        0.2211 0.1704 0.2636]';

    clc;

end

if ( size(XData) ~= size(YData) )

    error('The two input matrices have to be of the same sizes');

end

plot(XData,YData,'ro'); hold on; h = plot(XData,YData,'b'); hold off;

title('Input data');

if ( size(XData, 1) == 1 )

    t = XData';

    y = YData';

elseif(size(XData, 2) == 1)

    t = XData;

    y = YData;

end

%%

% The goal is to fit the following function with two linear parameters and two

% nonlinear parameters to the data:

%

%     y =  C(1)*exp(-lambda(1)*t) + C(2)*exp(-lambda(2)*t)

%

% To fit this function, we've create a function FITFUN.  Given the nonlinear

% parameter (lambda) and the data (t and y), FITFUN calculates the error in the

% fit for this equation and updates the line (h).

% type fitfun

%%

% Make a guess for initial estimate of lambda (start) and invoke FMINSEARCH.  It

% minimizes the error returned from FITFUN by adjusting lambda.  It returns the

% final value of lambda.

start = [1;0];

options = optimset('TolX',0.1);

estimated_lambda = fminsearch('fitfun',start,options,t,y,h);

%estimated_lambda = [estimated_lambda guidata(gcf)];

coeff = guidata(h); % C1,C2

'estimated_lambda = estimated_lambda';

'coeff = coeff';

要拟合的函数为

function err = fitfun(lambda,t,y,handle)

%FITFUN Used by FITDEMO.

%   FITFUN(lambda,t,y,handle) returns the error between the data and the values

%   computed by the current function of lambda.

%

%   FITFUN assumes a function of the form

%

%     y =  c(1)*exp(-lambda(1)*t) + ... + c(n)*exp(-lambda(n)*t)

%

%   with n linear parameters and n nonlinear parameters.

%   Copyright 1984-2002 The MathWorks, Inc.

%   $Revision: 5.8 $  $Date: 2002/04/08 20:04:42 $

A = zeros(length(t),length(lambda));

for j = 1:length(lambda)

   A(:,j) = exp(-lambda(j)*t);

end

c = A\y;

z = A*c;

err = norm(z-y);

set(gcf,'DoubleBuffer','on');

set(handle,'ydata',z)

guidata(handle, c);

drawnow

pause(.04)

因为这种拟合方法用的比较少，所以特此总结出来，希望后来对自己的道路段的拟合会有用处！

2016-1-23 1059