Fitting multiple datasets to non-linear coupled ODE's - fminsearch

I have attached my code for reference.

Essentially, I have two datasets: ch and cm; the former has an additional datapoint (a 12th day).

There are two unknown parameters, beta1 and beta2.

Essentially I want to fit my model to these datasets simultaneously, where I use the least-squares difference method to calculate the error [line 85-95].

When fitting to a single dataset, I understand the aim is to minimise the error. However, in this case I (maybe naively) have just computed a total error by adding these two together.

The code runs perfectly fine, I just wanted to make sure I was doing things correctly.

Answer 1

[Alan Stevens]

Why not just use

errT=norm(cellHND - chND)+norm(cellLND - clND);

instead of looping through the sums.

[Alistair McQueen]

Honestly, I am not sure. Thanks though, yields a similar result, varying (I assume) depending on the error calculation used.

I assume this is an adequate way to calculate the total error when considering a fit to multiple datasets?

As my most recent approach to modelling the error is:

errT = sum((((ch(j)-cellH(j))/ch(j))).^2 + (((cl(j)-cellL(j))/cl(j))).^2);