![]() Give this a try and let us know what you think here or leave a comment for Are. If you need other sophisticated constraints, you would want to check out Optimization Toolbox. You can conduct regression analysis using the library of linear and nonlinear models provided. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. norm(x_lsqlin2-x_polyfix')Īre's polyfit is great for performing polynomial fits with constraints around passing points. Curve Fitting Toolbox provides an app and functions for fitting curves and surfaces to data. If we compare this result with that from lsqlin, we see that it's essentially identical. ![]() Set(gca, 'XTick',0:0.5:2) % Adjust tick marks to show points of interest x_polyfix = Let's solve the same problem using polyfix, 7th order polynomial to fit through (0,0), (2,0), (0.5,1), (1.5,-1) and derivative of zero at $t$ = 0.5 and $t$ = 1.5. This entry achieves the goal of performing a polynomial fit with constraints to pass through specific points with specific derivatives. In the MATLAB Answers post I mentioned above, Are actually posted a response mentioning polyfix. Set(gca, 'XTick',0:0.5:2) % Adjust tick marks to show points of interest Warning: The trust-region-reflective algorithm can handle bound constraints Legend( 'data', 'lsqlin (with derivative constraint)') Let's fit and see if we've accomplished our goal. lsqlin solves the following least-squares curve fitting problem. There are several ways to deal with this, and one of them is to use a function like lsqlin from Optimization Toolbox. But first, let me talk about a different method. This is where Are's entry comes into play. Perhaps, you want the curve to cross (0, 0) and (2, 0). ![]() What if you want this polynomial to go through certain points. We'll fit a 3rd order polynomial to the data. The function polyfit lets you fit a polynomial to your data.
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