Optimization ODE using Simulated Annealing
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Hello,
I am working on finding the optimum values of 26 parameter (P) in a system ODE using SA. The code I am following is presented in this question. So, I tried to change the objective function to the equation shown in the picture. However, I wasn't able to. Are there any suggections to how I can solve it?
Z(P) = ZP = the estimated parameters in a vector
Z= Z1= experimental values of the parameters
P = vector of 26 parameters

function a = objective(P)
% Experimental Data
Z1=[B1,N1,Nq1,A1,S1,L1,AB1]; % size(8X7)where each element is a vector with 8 elements
% initial values of the 7 variables
y01=[0.2;0.124;0.2;0.03;0.0045;0.00348;0.19];
tspan=[0;48;96;144;192;240;288;480];
[t,V]= ode45(@equations,tspan,y01); % @equations is the ode function including 26 parameter
ZP= V.';
% calculate the objective function
a= 0;
for h=1:8
for l=1:7
a= a +((ZP(l,h)-Z1(l,h))/Z1(l,h))^2;
end
end
end
%%%
% the optimization call
ftns= @(P) objective(P);
[P,error,exitflag,output] = simulannealbnd(ftns,P0,lb,ub,options);
2 个评论
Star Strider
2020-8-16
The simulannealbnd function is going to attempt to minimise ‘objective’ by adjusting the ‘P’ parameters. However no ‘P’ parameter ever appears in the code in ‘objective’.
Note that neither ‘equations’ nor the elements of ‘Z1’ are defined. They must either be defined or be passed specifically as additional arguments to ‘objective’, since they will not automatically be inherited from the calling workspace.
Amal Ashoor
2020-8-17
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