fmincon() is a very good general purpose routine to find the parameters that give a good fit to your data.
To use fmincon(), you need to write a function that returns the error between your data and the predictions from your equation. Usually the error is defined as the sum of the squared errors between the data and the predicitons. You need to pass to fmincon() the name of that function, and an initial guess for the parameters, and some bounds on the parameters (i.e a vector of the maximum acceptable value of each parameter and a vector of the minimum acceptable value for each). Then fmincon() searches parameter space to find the set of parameters that minimize the error and which obey the maximum and minimum constraints.
You already have a function to fit the data (in your image file). It has 3 adjustable parameters: 
. Use that to fit the data. Do you need another functionto compare? If so, try a straight line.
. Use that to fit the data. Do you need another functionto compare? If so, try a straight line. 
