Data Regression with MATLAB

MATLAB Data Regression

A frequent activity for scientists and engineers is to develop correlations from data. By importing the data into MATLAB, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. This tutorial demonstrates how to create a linear, polynomial, or nonlinear function that best approximates the data trend and how to analyze the solution. Script files of the MATLAB source code with sample data are below.

Linear and Polynomial Regression

MATLAB Data Regression Example Source Code

Nonlinear Regression with APM MATLAB

MATLAB Nonlinear Regression Example Source Code

Nonlinear Regression with MATLAB fmincon

% clear session, close plots, clear screen
clear all; close all; clc
% data for regression
xm = [18.3447,79.86538,85.09788,10.5211,44.4556, ...
69.567,8.960,86.197,66.857,16.875, ...
52.2697,93.917,24.35,5.118,25.126, ...
34.037,61.4445,42.704,39.531,29.988];
ym = [5.072,7.1588,7.263,4.255,6.282, ...
6.9118,4.044,7.2595,6.898,4.8744, ...
6.5179,7.3434,5.4316,3.38,5.464, ...
5.90,6.80,6.193,6.070,5.737];
% initial parameter guess
p0 = [1,0.5,1];
% define prediction function
yp = @(p) p(1) + p(2)./xm + p(3).*log(xm);
% define objective function (scaled sum of squared errors)
objective = @(p) sum(((yp(p)-ym)./ym).^2);
disp(['Initial Objective: ' num2str(objective(p0))])

% optimize with fmincon
%[X,FVAL,EXITFLAG,OUTPUT,LAMBDA,GRAD,HESSIAN]
% = fmincon(FUN,X0,A,B,Aeq,Beq,LB,UB,NONLCON,OPTIONS)
A = [];
b = [];
Aeq = [];
beq = [];
% bounds
lb = []; % ones(3)*0.2;
ub = []; % ones(3)*1.5;
popt = fmincon(objective,p0,A,b,Aeq,beq,lb,ub);

% print results
disp(['Final Objective: ' num2str(objective(popt))])
disp(['Optimal parameters: ' num2str(popt)])

% plot results
plot(xm,ym,'ro')
hold on
plot(xm,yp(p0),'bx')
plot(xm,yp(popt),'gs')
legend('measured','initial predicted','optimal predicted')
ylabel('y')
xlabel('x')

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