differential evalution code Error using * Inner matrix dimensions must agree.

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common fernando
common fernando 2020-12-2
编辑： Stephan 2020-12-3
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);
  1. clear all; close all; clc
  3. fs=200; %sampling freq.
  4. dt =1/fs;
  5. n=fs/3 %number of samples/cycle
  6. m=3 %no. of cycles
  7. fi=50;
  8. t = dt*(0:400); %data window
  10. ww=wgn(201,1,-40);
  11. size(transpose(ww))
  12. t =dt*(0:200);
  13. y=sin(2*pi*fi*t + 0.3);
  14. for j=0:200/(n*m)
  15. t =dt*(j*m*n:(j+1)*m*n);
  16. x=sin(2*pi*fi*t + 0.3)+transpose(wgn(1+n*m,1,-40));
  17. V=x
  19. tmax=0.01;
  20. lastreported=0;
  21. %% Problem Definition
  22. t_est=[];
  23. f_est=[];
  24. dt=1/fs;
  25. i_max=tmax*fs
  26. for ii=0:i_max
  27. if(ii/i_max*100-lastreported>=1)
  28. lastreported=ii/i_max*100;
  29. fprintf('%5.2f%%\n',lastreported);
  30. end
  31. t=(ii:ii+n-1)*dt;
  33. CostFunction=@(x) sum((minus((x(1)),V)*sin(2*pi.*x(2).*t+x(3)).^2))/n; % Cost Function
  34. nVar=3; % Number of Decision Variables
  35. VarSize=[1 nVar]; % Decision Variables Matrix Size
  36. VarMin=[0,48,0]; % Lower Bound of Decision Variables
  37. VarMax=[1000,52,2*pi]; % Upper Bound of Decision Variables
  39. %% DE Parameters
  41. MaxIt=200; % Maximum Number of Iterations
  42. nPop=50; % Population Size
  43. beta=0.5; % Scaling Factor
  44. pCR=0.2; % Crossover Probability
  45. minCost=1e-10;
  46. %% Initialization
  48. empty_individual.Position=[];
  49. empty_individual.Cost=[];
  51. BestSol.Cost=inf;
  52. pop=repmat(empty_individual,nPop,1);
  54. for i=1:nPop
  55. pop(i).Position=unifrnd(VarMin,VarMax,VarSize);
  56. pop(i).Cost=CostFunction(pop(i).Position);
  57. if pop(i).Cost<BestSol.Cost
  58. BestSol=pop(i);
  59. end
  60. end
  61. BestCost=zeros(MaxIt,1);
  63. %% DE Main Loop
  65. for it=1:MaxIt
  66. for i=1:nPop
  67. x=pop(i).Position;
  68. A=randperm(nPop);
  69. A(A==i)=[];
  70. a=A(1);
  71. b=A(2);
  72. c=A(3);
  73. % Mutation
  74. %beta=unifrnd(beta_min,beta_max);
  75. y=pop(a).Position+beta.*(pop(b).Position-pop(c).Position);
  76. y = max(y, VarMin);
  77. y = min(y, VarMax);
  78. % Crossover
  79. z=zeros(size(x));
  80. j0=randi([1 numel(x)]);
  81. for j=1:numel(x)
  82. if j==j0 || rand<=pCR
  83. z(j)=y(j);
  84. else
  85. z(j)=x(j);
  86. end
  87. end
  88. NewSol.Position=z;
  89. NewSol.Cost=CostFunction(NewSol.Position);
  91. if NewSol.Cost<pop(i).Cost
  92. pop(i)=NewSol;
  93. if pop(i).Cost<BestSol.Cost
  94. BestSol=pop(i);
  95. end
  96. end
  98. end
  100. % Update Best Cost
  101. BestCost(it)=BestSol.Cost;
  102. % Show Iteration Information
  103. %disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
  104. if(minCost>BestSol.Cost)
  105. break;
  106. ErrorTarget=0.00000001;
  107. EvalMax=10000*n;
  108. end
  110. end
  111. %% Show Results
  112. % disp(['Iteration ' num2str(ii) ': Best Cost = ' num2str(BestSol.Position(2))]);
  113. t_est=[t_est;(ii)*dt];
  114. f_est=[f_est;BestSol.Position(2)];
  115. if(minCost>BestSol.Cost)
  116. %break;
  117. ErrorTarget=0.00000001;
  118. EvalMax=10000*n;
  119. end
  120. end
  121. end
  122. t_est
  123. f_est
  124. plot (t_est,f_est,'red')
  126. hold on
  128. xlabel('time')
  129. ylabel('frequency')
  130. title('DE white noise ')
  131. c=vpa(rms(fi(t_est)-f_est))
  132. plot (t_est,fi*ones(size(t_est)))
  133. hold off

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Stephan
Stephan 2020-12-2
@(x)sum((minus((x(1)),V).*sin(2.*pi.*x(2).*t+x(3)).^2))/n
% ^
% |
% ------- Elementwise multiplication
  19 个评论
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Stephan
Stephan 2020-12-3
编辑：Stephan 2020-12-3
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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