Optimizing parameters in ODE

Hi I have a set of experimental data. I want to fit this experimental data to first order differential equation of the form dy/dt = -a*n-b*n^2-c*n^3 to optimize the value of constants a,b and c. Can anyone help in this regards? I am new to matlab as this question might be too simple for others. Thanks in advance.

Answer 1

[Star Strider]

This is a simple, separable differential equation that you can likely solve by hand.

Using the Symbolic Math Toolbox:

syms a b c n y(t) y0 
DEqn = diff(y) == -a*n-b*n^2-c*n^3;
Eqn = dsolve(DEqn, y(0)==y0)
fcn = matlabFunction(Eqn, 'Vars',{[a,b,c],t,n,y0})

produces:

Eqn =
y0 - t*(c*n^3 + b*n^2 + a*n)
 
fcn =
  function_handle with value:
    @(in1,t,n,y0) y0-t.*(in1(:,1).*n+in1(:,2).*n.^2+in1(:,3).*n.^3)

or more conveniently:

    fcn = @(in1,t,n,y0) y0-t.*(in1(:,1).*n+in1(:,2).*n.^2+in1(:,3).*n.^3);

with ‘in1’ corresponding to [a,b,c] in that order. Supply values for ‘n’ and ‘y0’, then present it to the nonlinear parameter estimation function of your choice as:

    objfcn = @(in1,t) fcn(in1,t,n,y0)

Or, since it is ‘linear in the parameters’ you can re-write it as a design matrix and use linear methods such as mldivide,\ to solve it as well.

[Bibek Dhami]

Hi Star Strider

Sorry my differential equation was of the form dn/dt = -a*n-b*n^2-c*n^3. I have experimental values of n and t. I am new to matlab so I got stuck in your code. Thanks

[Star Strider]

It would have been helpful to know that yesterday. Your differential equation is nonnlinear, so like most such, there is no symbolic solution to it. You will have to integrate it numerically in order to fit it to your data.

Try this:

    function N = objfcn(b,t)
        n0 = b(4);
        DifEq = @(t,n) -b(1).*n - b(2).*n.^2 - b(3).*n.^3;
        [t,N] = ode45(DifEq,t,n0);
    end

t = (0:9)';                                                     % Create Data
data = randn(size(t));                                          % Create Data

B0 = ones(1,4);

[B,Rsdnrm,Rsd,ExFlg,OptmInfo,Lmda,Jmat]=lsqcurvefit(@objfcn,B0,t,data);

prmnam = {'a','b','c','ic'};
fprintf(1,'\tParameters:\n')
for k = 1:length(B)
    fprintf(1, '\t\t%s\t = %23.15E\n', prmnam{k}, B(k))
end

tv = linspace(min(t), max(t));
Cfit = objfcn(B, tv);

figure(1)
plot(t, data, 'p')
hold on
hlp = plot(tv, Cfit);
hold off
grid
xlabel('Time')
ylabel('Value')

This ran without error when I experimented with it.

Save the ‘objfcn’ function file to a separate file called objfcn.m on your MATLAB user path.

Remember that ‘data’ (the vector you want to fit) must be a column vector. Transpose it to be such if necessary.

You may need to experiment with different values of ‘B0’ (the initial parameter estimates) to get a good fit to your data. Nonnlinear parameter estimation is very sensitive to the initial parameter estimates, so several attempts may be necessary if you do not already have a good idea of what your parameters should be.

[Bibek Dhami]

Thank you Star Strider for your code. It helped me a lot though I am stuck in the initial condition of parameters to exactly fit it.

[Star Strider]

As always, my pleasure!

My code estimates the initial condition as well, estimating it as ‘b(4)’, in the printed results as ‘ic’. So an initial estimate for it shoulld be ‘B0(4)’.