The simplest way to curve fit multiple equations while conserving some parameter values is by converting the problem into matrix form. For example, for a set of two equations such as:

we may write the solution matrix as:

Curve fitting functions are generally using local optimization techniques such as minimizing sum of squares.

Here, *i *is the solution of the X equation at the time point and the corresponding data point.

We can exploit this to miminize a matrix of values by providing a matrix of data and a matrix of solutions. For data sets corresponding to X and Y, concatenate the vectors into a matrix:

The output of the solutions to X(t) and Y(t) with the current set of parameter values can also be put into a matrix. Then the sum of squares can be solved on the matrices, allowing for parameter values to be conserved across equations.

where **M **and **V ** are the parameter and data matrices respectively.

For the initial estimates of the parameters, I’ve had success with separately curve fitting each equation separately in a user-friendly software such as JMP. For difficult equations such as logarithms, I’ve found that setting a selection of parameters to zero allows for good initial estimates. In this example system:

### Like this:

Like Loading...

*Related*