Maximum Likelihood Estimator (MLE) and it is. I have attached the obtained surfplot (SSE is the function to be minimized, a, d, sigma are parameters that I want to obtain by minimization). A template for parameter estimation with Matlab Optimization Toolbox including dynamic systems. ![]() One suggestion was to do a grid search for the global minima and so first I tried to plot the function that I want to optimize vs two of my fitting parameters (so, the other two are constant) to have a sense of what is going on. The output acov is a p -by- p matrix, where p is the number of parameters in params. However, I get close or similar results as the local solvers even by using multistart and globalsearch. acov mlecov (params,data,'pdf',pdf) returns an approximation to the asymptotic covariance matrix of the maximum likelihood estimators of the parameters for a distribution specified by the custom probability density function pdf. So, I thought it would be helpful to see the result of globalsearch and multistart in the optimization toolbox and I tried it by using different solvers and algorithms. Also, I do have 4 fitting parameters and everytime automatically it assumes two of them are fixed at startpoint and only fits the other two (It is not too bad because the two fixed parameters are more or less predictable from the fits). But the result is changing each time I change the startpoints. It enables you to find optimal solutions in applications such as portfolio optimization, energy management and trading, and production planning. I tried 'Levenberg-Marquardt' algorithm in the curve fitting toolbox. The toolbox lets you perform design optimization tasks, including parameter estimation, component selection, and parameter tuning. What I have noticed in my fits is I do get inconsistent results (including this discrepancy for SSE) for some of data series. But from your example and explanation it seems to me it will not make a huge change. So, I was thinking maybe I should minimize something else (i.e. I have also tried the optimization toolbox algorithms and solvers and I also tried to minimize SSE function there but it didn't help. ![]() SSE) is not the best fit in terms of what I see and also the calculated efficiency. I think one problem is I see the fitted curve with better goodness of fit measures (i.e. The problem is for some of these series of data, I don't obtain a good fit and so the obtained efficiency is far different from the experiment. I am trying to fit each series of data which is taken at different frequency domains to a function and obtain the value of the fitting parameters (I have 4 fitting parameters) and use the obtained values of the fitting parameters to calculate the efficiency of the system by another equation and compare it with the experimental efficiency. I have 15 series of experimental data taken at different frequencies.
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