On Monday, September 2, 2013 1:41:34 AM UTC+10, Alan_Weiss wrote:
> On 9/1/2013 3:16 AM, Kapila Bandara wrote:
>
> > i want to know how can i put constraint in curve fitting optimization function.
>
> >
>
> > For example i want to give constraint as 0<beta <1 in following function.
>
> >
>
> > .......................................................
>
> > function [estimates, model] = fitcurvedemo(xdata, ydata)
>
> > % Call fminsearch with a random starting point.
>
> > start_point = rand(1,4 );
>
> > model = @expfun;
>
> > estimates = fminsearch(model, start_point);
>
> > % expfun accepts curve parameters as inputs, and outputs sse,
>
> > % the sum of squares error for A*exp(lambda*xdata)ydata,
>
> > % and the FittedCurve. FMINSEARCH only needs sse, but we want
>
> > % to plot the FittedCurve at the end.
>
> > function [sse, FittedCurve] = expfun(params)
>
> > A = params(1);
>
> > lambda = params(2);
>
> > B=params(3);
>
> > beta=params(4);
>
> > FittedCurve = (A .* exp(lambda * xdata) +B.*xdata.^beta);
>
> > ErrorVector = FittedCurve  ydata;
>
> > sse = sum(ErrorVector .^ 2);
>
> > end
>
> > end
>
>
>
> You can use fmincon instead of fminsearch if you have Optimization Toolbox.
>
>
>
> Or, for this simple constraint, use exp(beta^2) where you currently
>
> have beta in FittedCurve. exp(beta^2) is always between 0 and 1. I
>
> recommend you start beta out with a value of 1, not something close to 0
>
> or higher than 2.
>
>
>
> Alan Weiss
>
> MATLAB mathematical toolbox documentation
Thank you very much it is working
