How can I use Genetic Algorithm toolbox to calibrate a model?
Show older comments
Hi, this is my first time I use Optimize Live Editor Task.
I have a function, which calculates some outputs throughot the time,
[zz,Az,Vz,tt,Qst,Kzt,Tzt,Czt,Szt,Pzt,Chlzt,PPzt,DOPzt,DOCzt,Qzt_sed,lambdazt,...
P3zt_sed,P3zt_sed_sc,His,DoF,DoM,MixStat,Wt]...
= solvemodel_v12(m_start,m_stop,initfile,'lake',inputfile,'timeseries', parafile,'lake');
Each output is calculated throughout the time step (if it is a year, we would have 365 values for every output).
I have the observation of an output Tzt, and now I want to calibrate my model to minimize RMSQ, and during the calibration process, I would like parameters in the parafile excelsheet to be changed, which its maximum and minimum values are also indicated in that excelsheet. I don't know how I can use FA in live editor task to calibrate this model.
Answers (1)
Alan Weiss
on 28 Feb 2022
Edited: Alan Weiss
on 28 Feb 2022
It is entirely possible that I have misunderstood your question in some way. Feel freee to ask again with more explanation if my answer is not relevant.
First, it is not at all clear to me that ga is the best tool for this task. Why not use lsqnonlin, which is generally faster and more accurate?
But if you have a good reason for using ga then you can write the objective as something like
function Tzt = ...
myfun(m_start,m_stop,initfile,'lake',inputfile,'timeseries', parafile,'lake');
[~,~,~,~,~,~,Tzt,~,~,~,~,~,~,~,~,~,~,~,~,~,~,~,~] = ...
solvemodel_v12(m_start,m_stop,initfile,'lake',inputfile,'timeseries', parafile,'lake');
end
Call ga like this:
fun = @(parafile)sum((myfun(m_start,m_stop,initfile,'lake',inputfile,'timeseries', parafile,'lake') ...
- (theExpectedValue)).^2);
[sol,fval,efag,output] = ga(fun,nvar,A,b,Aeq,beq,lb,ub,nlcon)
Put in any necessary constraints in the ga call as well.
The solution sol has the new values for the parafile variables. You can write this out to the Excel file after ga runs.
Good luck,
Alan Weiss
MATLAB mathematical toolbox documentation
6 Comments
Sahar khalili
on 28 Feb 2022
Alan Weiss
on 28 Feb 2022
Sorry, I do not understand what you are asking, nor what you didn't like in my previous answer. I might not be able to help you.
Alan Weiss
MATLAB mathematical toolbox documentation
Sahar khalili
on 28 Feb 2022
Alan Weiss
on 1 Mar 2022
Since you only want three parameters changed out of many, you can modify the script just a bit.
You need to extract the parafile values and edit them within your function. Suppose that you have paraval as the MATLAB variable containing data. You use this as an unchanging data value that you pass to your objective function. I also suppose that you have the expected values that you are trying to match as the vector theExpectedValue.
function val = sumsq(params,paraval,theExpectedValue)
% Put the params values into paraval, using the appropriate mapping
paraval(10) = params(1);
paraval(14) = params(2);
paraval(15) = params(3);
% Evaluate your objective at paraval
values = solvemodel_v12(m_start,m_stop,initfile,'lake',inputfile,'timeseries', paraval,'lake');
% Compute the sum of squares differences
val = sum((values - theExpectedValue).^2);
end
Call the minimization like this:
fun = @(params)sumsq(params,paraval,theExpectedValue);
[sol,fval] = ga(fun,3,A,b,Aeq,beq,lb,ub);
% Here lb and ub apply only to the 3-D vector params
% You may well have A, b, Aeq, and beq all = []
Again, I might have misunderstood something, but this is the solution as I understand it.
Alan Weiss
MATLAB mathematical toolbox documentation
Sahar khalili
on 1 Mar 2022
Alan Weiss
on 3 Mar 2022
Yes, you have to get the data into MATLAB.
Alan Weiss
MATLAB mathematical toolbox documentation
Categories
Find more on Problem-Based Optimization Setup in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!