使用输出函数绘制中间拟合。start = [1;0];% We use an anonymous function to pass additional parameters t, y, h to the% output function.outputFcn = @(x,optimvalues,state) fitoutputfun(x,optimvalues,state,t,y,h);options = optimset('OutputFcn',outputFcn,'TolX',0.1);...
params = lsqcurvefit(model, initial_guess, x_data, y_data, [], [], options); % 构建拟合后的二次函数表达式 fitted_function = @(x) params(1) * x.^2 + params(2) * x + params(3); % 绘制原始数据和拟合曲线figure; plot(x_data, y_data, 'ro'); % 绘制原始点 hold on; xx = ...
matlab中plot函数的使用(TheuseofplotfunctionsinMATLAB) Fifth,thevisualizationofcalculationresults ThissectiondescribestwobasicgraphicsfunctionsofMATLAB: two-dimensionalgraphicandthree-dimensionalgraphics. 5.1two-dimensionalplanefigure 5.1.1basicgraphicsfunction Plotisthemostbasicfunctionofdrawingtwo-dimensional graphics,...
plot(x,y_fit,'m','LineWidth',2);holdofflegend('噪声数据','理想数据','拟合数据');%%%%%%%...
handle, or a cell array of built-in plot function names or function handles. For custom plot functions, pass function handles. The default is none ([]):‘PlotFcns’这行代码是用来画计算过程中的数据的，optimplotx @optimplotfval 分别是迭代过程中输入的X值，和相应的目标函数值 ...
function[th,err,yi]=polyfits(x,y,N,xi,r)% x,y:数据点系列% N:多项式拟合的系统% r:加权系数的逆矩阵M =length(x); x = x(:); y = y(:);% 判断调用函数的格式ifnargin ==4% 当调用的格式为 (x,y,N,r)iflength(xi) == M ...
function out = scatplot(x,y,method,radius,N,n,po,ms) % Scatter plot with color indicating data density % % USAGE: % out = scatplot(x,y,method,radius,N,n,po,ms) % out = scatplot(x,y,dd) % % DESCRIPTION: % Draws a scatter plot with a colorscale ...
Options 选项 population 种群 Population type 编码类型 Double vector 实数编码。采用双精度,整数规划的种群类型必须是实数编码。 Bit string 二进制编码 Custom 自定义 Population size 种群大小 Creation function 生成函数,产生初始种群 Initial population 初始种群,不指定则使用creation function生成,可以指定少于种群数量...
2、优化选项( Options )Stopping criteria: 停止准则。Function value check: 函数值检查。User-supplied derivatives:用户自定义微分(或梯度) 。Approximated derivatives:自适应微分(或梯度) 。Algorithm settings: 算法设置。Inner iteration stopping c 5、riteria: 内迭代停止准则。Plot function: 用户自定义绘图函数...
[xData,yData]=prepareCurveData(x1,y1);%Set up fittype and options.ft=fittype('smoothingspline');%Fit model to data.[fitresult,gof]=fit(xData,yData,ft);%Plot fitwithdata.figure('Name','untitled fit 1');h=plot(fitresult,xData,yData);legend(h,'y1 vs. x1','untitled fit 1','...