differential evalution code Error using * Inner matrix dimensions must agree.
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common fernando
am 2 Dez. 2020
Bearbeitet: Stephan
am 3 Dez. 2020
Error in @(x)sum((minus((x(1)),V)*sin(2.*pi.*x(2).*t+x(3)).^2))/n Error in noise_de (line 56) pop(i).Cost=CostFunction(pop(i).Position);
- clear all; close all; clc
- fs=200; %sampling freq.
- dt =1/fs;
- n=fs/3 %number of samples/cycle
- m=3 %no. of cycles
- fi=50;
- t = dt*(0:400); %data window
- ww=wgn(201,1,-40);
- size(transpose(ww))
- t =dt*(0:200);
- y=sin(2*pi*fi*t + 0.3);
- for j=0:200/(n*m)
- t =dt*(j*m*n:(j+1)*m*n);
- x=sin(2*pi*fi*t + 0.3)+transpose(wgn(1+n*m,1,-40));
- V=x
- tmax=0.01;
- lastreported=0;
- %% Problem Definition
- t_est=[];
- f_est=[];
- dt=1/fs;
- i_max=tmax*fs
- for ii=0:i_max
- if(ii/i_max*100-lastreported>=1)
- lastreported=ii/i_max*100;
- fprintf('%5.2f%%\n',lastreported);
- end
- t=(ii:ii+n-1)*dt;
- CostFunction=@(x) sum((minus((x(1)),V)*sin(2*pi.*x(2).*t+x(3)).^2))/n; % Cost Function
- nVar=3; % Number of Decision Variables
- VarSize=[1 nVar]; % Decision Variables Matrix Size
- VarMin=[0,48,0]; % Lower Bound of Decision Variables
- VarMax=[1000,52,2*pi]; % Upper Bound of Decision Variables
- %% DE Parameters
- MaxIt=200; % Maximum Number of Iterations
- nPop=50; % Population Size
- beta=0.5; % Scaling Factor
- pCR=0.2; % Crossover Probability
- minCost=1e-10;
- %% Initialization
- empty_individual.Position=[];
- empty_individual.Cost=[];
- BestSol.Cost=inf;
- pop=repmat(empty_individual,nPop,1);
- for i=1:nPop
- pop(i).Position=unifrnd(VarMin,VarMax,VarSize);
- pop(i).Cost=CostFunction(pop(i).Position);
- if pop(i).Cost<BestSol.Cost
- BestSol=pop(i);
- end
- end
- BestCost=zeros(MaxIt,1);
- %% DE Main Loop
- for it=1:MaxIt
- for i=1:nPop
- x=pop(i).Position;
- A=randperm(nPop);
- A(A==i)=[];
- a=A(1);
- b=A(2);
- c=A(3);
- % Mutation
- %beta=unifrnd(beta_min,beta_max);
- y=pop(a).Position+beta.*(pop(b).Position-pop(c).Position);
- y = max(y, VarMin);
- y = min(y, VarMax);
- % Crossover
- z=zeros(size(x));
- j0=randi([1 numel(x)]);
- for j=1:numel(x)
- if j==j0 || rand<=pCR
- z(j)=y(j);
- else
- z(j)=x(j);
- end
- end
- NewSol.Position=z;
- NewSol.Cost=CostFunction(NewSol.Position);
- if NewSol.Cost<pop(i).Cost
- pop(i)=NewSol;
- if pop(i).Cost<BestSol.Cost
- BestSol=pop(i);
- end
- end
- end
- % Update Best Cost
- BestCost(it)=BestSol.Cost;
- % Show Iteration Information
- %disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
- if(minCost>BestSol.Cost)
- break;
- ErrorTarget=0.00000001;
- EvalMax=10000*n;
- end
- end
- %% Show Results
- % disp(['Iteration ' num2str(ii) ': Best Cost = ' num2str(BestSol.Position(2))]);
- t_est=[t_est;(ii)*dt];
- f_est=[f_est;BestSol.Position(2)];
- if(minCost>BestSol.Cost)
- %break;
- ErrorTarget=0.00000001;
- EvalMax=10000*n;
- end
- end
- end
- t_est
- f_est
- plot (t_est,f_est,'red')
- hold on
- xlabel('time')
- ylabel('frequency')
- title('DE white noise ')
- c=vpa(rms(fi(t_est)-f_est))
- plot (t_est,fi*ones(size(t_est)))
- hold off
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Akzeptierte Antwort
Stephan
am 2 Dez. 2020
@(x)sum((minus((x(1)),V).*sin(2.*pi.*x(2).*t+x(3)).^2))/n
% ^
% |
% ------- Elementwise multiplication
19 Kommentare
Stephan
am 3 Dez. 2020
Bearbeitet: Stephan
am 3 Dez. 2020
The problem is here:
t_est=[t_est;(ii+n-1)*dt];
this leads to values of t_est ranging from 0...1 - not 0...200 as expected. If you change it to:
t_est=[t_est; (ii-1)*dt*fs];
The values are scaled 0...200. But due to missing insight in the problem i dont know if this is correct. At least the plot seems to be ok.
Since fi is ascalar, it wont plot - i suggest to use yline instead:
plot(t_est,f_est,'red')
xlim([0 200])
yline(fi)
xlabel('time')
ylabel('frequency')
title('DE white noise')
So once again the whole code:
clear all; close all; clc
fs=200; %sampling freq.
dt =1/fs;
n=fs/3 %number of samples/cycle
m=3 %no. of cycles
fi=50;
t = linspace(0,200,1+n*m); %data window
v=@(t) (sin(2*pi*fi.*t + 0.3)+ transpose(wgn(1+n*m,1,-40)));
tmax=1;
t_est=[];
f_est=[];
dt=1/fs;
i_max=tmax*fs;
for ii=0:i_max
V=v(t);
CostFunction=@(x) sum((V-x(1)*sin(2*pi*x(2)*t+x(3))).^2)/n; % Cost Function
nVar=3; % Number of Decision Variables
VarSize=[1 nVar]; % Decision Variables Matrix Size
VarMin=[0,48,0]; % Lower Bound of Decision Variables
VarMax=[1000,52,2*pi]; % Upper Bound of Decision Variables
%% DE Parameters
MaxIt=1000; % Maximum Number of Iterations
nPop=50; % Population Size
beta=0.5; % Scaling Factor
pCR=0.2; % Crossover Probability
minCost=1e-10;
%% Initialization
empty_individual.Position=[];
empty_individual.Cost=[];
BestSol.Cost=inf;
pop=repmat(empty_individual,nPop,1);
for i=1:nPop
pop(i).Position=unifrnd(VarMin,VarMax,VarSize);
pop(i).Cost=CostFunction(pop(i).Position);
if pop(i).Cost<BestSol.Cost
BestSol=pop(i);
end
end
BestCost=zeros(MaxIt,1);
%% DE Main Loop
for it=1:MaxIt
for i=1:nPop
x=pop(i).Position;
A=randperm(nPop);
A(A==i)=[];
a=A(1);
b=A(2);
c=A(3);
% Mutation
%beta=unifrnd(beta_min,beta_max);
y=pop(a).Position+beta.*(pop(b).Position-pop(c).Position);
y = max(y, VarMin);
y = min(y, VarMax);
% Crossover
z=zeros(size(x));
j0=randi([1 numel(x)]);
for j=1:numel(x)
if j==j0 || rand<=pCR
z(j)=y(j);
else
z(j)=x(j);
end
end
NewSol.Position=z;
NewSol.Cost=CostFunction(NewSol.Position);
if NewSol.Cost<pop(i).Cost
pop(i)=NewSol;
if pop(i).Cost<BestSol.Cost
BestSol=pop(i);
end
end
end
% Update Best Cost
BestCost(it)=BestSol.Cost;
% Show Iteration Information
%disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
if(minCost>BestSol.Cost)
break;
end
end
%% Show Results
disp(['Iteration ' num2str(ii) ': Best Cost = ' num2str(BestSol.Position(2))]);
t_est=[t_est; (ii-1)*dt*fs];
f_est=[f_est;BestSol.Position(2)];
end
t_est
f_est
RMSE = sqrt(mean((f_est-fi).^2))
plot(t_est,f_est,'red')
xlim([0 200])
yline(fi)
xlabel('time')
ylabel('frequency')
title('DE white noise')
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