Hi Verena,
Each method uniquely addresses different modeling and optimization requirements.
greyest is designed for linear grey-box models with a known structure, making it highly effective for system identification by using input-output data to estimate parameters.
On the other hand, fmincon serves as a flexible nonlinear optimization tool, ideal for tackling problems with complex constraints by minimizing a scalar objective function within specified bounds.
In contrast, lsqnonlin is adept at handling nonlinear least-squares challenges, concentrating on reducing the sum of squared residuals, which makes it particularly well-suited for curve fitting tasks that do not involve extensive constraints.
You can refer to the following MathWorks documentations for more details:
- https://www.mathworks.com/help/optim/ug/lsqnonlin.html
- https://www.mathworks.com/help/ident/ref/greyest.html
- https://www.mathworks.com/help/optim/ug/fmincon.html
I hope this helps.
