This example shows how to fit a nonlinear function to data. For this example, the nonlinear function is the standard exponential decay curve

$$y(t)=A\mathrm{exp}(-\lambda t),$$

where $$y(t)$$ is the response at time $$t$$, and $$A$$ and $$\lambda $$ are the parameters to fit. Fitting the curve means finding parameters $$A$$ and $$\lambda $$ that minimize the sum of squared errors

$$\sum _{i=1}^{n}{({y}_{i}-A\mathrm{exp}(-\lambda {t}_{i}))}^{2},$$

where the times are $${t}_{i}$$ and the responses are $${y}_{i},i=1,\dots ,n$$. The sum of squared errors is the objective function.

Usually, you have data from measurements. For this example, create artificial data based on a model with $$A=40$$ and $$\lambda =0.5$$, with normally distributed pseudorandom errors.

rng default % for reproducibility tdata = 0:0.1:10; ydata = 40*exp(-0.5*tdata) + randn(size(tdata));

Write a function that accepts parameters `A`

and `lambda`

and data `tdata`

and `ydata`

, and returns the sum of squared errors for the model $$y(t)$$. Put all the variables to optimize (`A`

and `lambda`

) in a single vector variable (`x`

). For more information, see Minimizing Functions of Several Variables.

`type sseval`

function sse = sseval(x,tdata,ydata) A = x(1); lambda = x(2); sse = sum((ydata - A*exp(-lambda*tdata)).^2);

Save this objective function as a file named `sseval.m`

on your MATLAB® path.

The `fminsearch`

solver applies to functions of one variable, `x`

. However, the `sseval`

function has three variables. The extra variables `tdata`

and `ydata`

are not variables to optimize, but are data for the optimization. Define the objective function for `fminsearch`

as a function of `x`

alone:

fun = @(x)sseval(x,tdata,ydata);

For information about including extra parameters such as `tdata`

and `ydata`

, see Parameterizing Functions.

Start from a random positive set of parameters `x0`

, and have `fminsearch`

find the parameters that minimize the objective function.

x0 = rand(2,1); bestx = fminsearch(fun,x0)

`bestx = `*2×1*
40.6877
0.4984

The result `bestx`

is reasonably near the parameters that generated the data, `A = 40`

and `lambda = 0.5`

.

To check the quality of the fit, plot the data and the resulting fitted response curve. Create the response curve from the returned parameters of your model.

A = bestx(1); lambda = bestx(2); yfit = A*exp(-lambda*tdata); plot(tdata,ydata,'*'); hold on plot(tdata,yfit,'r'); xlabel('tdata') ylabel('Response Data and Curve') title('Data and Best Fitting Exponential Curve') legend('Data','Fitted Curve') hold off

- Optimizing Nonlinear Functions
- Nonlinear Data-Fitting (Optimization Toolbox)
- Nonlinear Regression (Statistics and Machine Learning Toolbox)