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

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

Answer 1

[Stephan]

@(x)sum((minus((x(1)),V).*sin(2.*pi.*x(2).*t+x(3)).^2))/n
%                       ^
%                       |
%                       ------- Elementwise multiplication

[common fernando]

what can I do

[Stephan]

Did you replace your function with my? I provided the corrected version.

[common fernando]

the error :

Error using +

Matrix dimensions must agree.

Error in @(t)(sin(2*pi*fi*t+0.3)+transpose(wgn(1+n*m,1,-40)))

Error in noise_de (line 23)

V=v(t);

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. v=@(t) (sin(2*pi*fi*t + 0.3)+ transpose(wgn(1+n*m,1,-40)));
12. tmax=1;
15. t_est=[];
16. f_est=[];
17. dt=1/fs;
18. i_max=tmax*fs;
19. for ii=0:i_max
20. t=(ii:ii+n-1)*dt;
21. V=v(t);
22. CostFunction=@(x) sum((V-x(1)*sin(2*pi*x(2)*t+x(3))).^2)/n; % Cost Function
23. nVar=3; % Number of Decision Variables
24. VarSize=[1 nVar]; % Decision Variables Matrix Size
25. VarMin=[0,48,0]; % Lower Bound of Decision Variables
26. VarMax=[1000,52,2*pi]; % Upper Bound of Decision Variables
28. %% DE Parameters
30. MaxIt=1000; % Maximum Number of Iterations
31. nPop=50; % Population Size
32. beta=0.5; % Scaling Factor
33. pCR=0.2; % Crossover Probability
34. minCost=1e-10;
35. %% Initialization
37. empty_individual.Position=[];
38. empty_individual.Cost=[];
40. BestSol.Cost=inf;
41. pop=repmat(empty_individual,nPop,1);
43. for i=1:nPop
44. pop(i).Position=unifrnd(VarMin,VarMax,VarSize);
45. pop(i).Cost=CostFunction(pop(i).Position);
46. if pop(i).Cost<BestSol.Cost
47. BestSol=pop(i);
48. end
49. end
50. BestCost=zeros(MaxIt,1);
52. %% DE Main Loop
54. for it=1:MaxIt
55. for i=1:nPop
56. x=pop(i).Position;
57. A=randperm(nPop);
58. A(A==i)=[];
59. a=A(1);
60. b=A(2);
61. c=A(3);
62. % Mutation
63. %beta=unifrnd(beta_min,beta_max);
64. y=pop(a).Position+beta.*(pop(b).Position-pop(c).Position);
65. y = max(y, VarMin);
66. y = min(y, VarMax);
67. % Crossover
68. z=zeros(size(x));
69. j0=randi([1 numel(x)]);
70. for j=1:numel(x)
71. if j==j0 || rand<=pCR
72. z(j)=y(j);
73. else
74. z(j)=x(j);
75. end
76. end
77. NewSol.Position=z;
78. NewSol.Cost=CostFunction(NewSol.Position);
80. if NewSol.Cost<pop(i).Cost
81. pop(i)=NewSol;
82. if pop(i).Cost<BestSol.Cost
83. BestSol=pop(i);
84. end
85. end
87. end
89. % Update Best Cost
90. BestCost(it)=BestSol.Cost;
91. % Show Iteration Information
92. %disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
93. if(minCost>BestSol.Cost)
94. break;
95. end
97. end
98. %% Show Results
99. disp(['Iteration ' num2str(ii) ': Best Cost = ' num2str(BestSol.Position(2))]);
100. t_est=[t_est;(ii+n-1)*dt];
101. f_est=[f_est;BestSol.Position(2)];
102. end

[common fernando]

I did it but I discover that I am not work in the first my code

so I resend it with intial error appear

[Stephan]

please resend it without the line numbers in front - and format it as code by using the button. We can not copy this and insert it to Matlab.

[common fernando]

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*(0:200); %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
        t=(ii:ii+n-1)*dt;
        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+n-1)*dt];
        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') 
hold on

xlabel('time')
ylabel('frequency')
title('DE white noise')

plot (t,fi)
hold off

[Stephan]

I do not have communications toolbox - but you should also use elementwise multiplication here:

v=@(t) (sin(2*pi*fi.*t + 0.3)+ transpose(wgn(1+n*m,1,-40)));
%                  ^
%                  |
%                  ------- Fixed here

Due to the missing toolbox i cant check if this is the only problem...

[common fernando]

the error in +

[common fernando]

when write ur code give me

Undefined function 'minus' for input arguments of type 'function_handle'.

Error in @(x)sum((minus((x(1)),V).*sin(2.*pi.*x(2).*t+x(3)).^2))/n

Error in finaldenoise (line 46)

pop(i).Cost=CostFunction(pop(i).Position);

[Stephan]

v=@(t) (sin(2*pi*fi.*t + 0.3)+ transpose(wgn(1+n*m,1,-40)));

No minus here. Is it again the wrong function like before?

[common fernando]

minus is here:

Error in @(x)sum((minus((x(1)),V).*sin(2.*pi.*x(2).*t+x(3)).^2))/n

thw wrong is :

Undefined function 'minus' for input arguments of type 'function_handle'.

Error in @(x)sum((minus((x(1)),V).*sin(2.*pi.*x(2).*t+x(3)).^2))/n

Error in finaldenoise (line 46)

pop(i).Cost=CostFunction(pop(i).Position);

>>

[common fernando]

minus is here:

Error in @(x)sum((minus((x(1)),V).*sin(2.*pi.*x(2).*t+x(3)).^2))/n

thw wrong is :

Undefined function 'minus' for input arguments of type 'function_handle'.

Error in @(x)sum((minus((x(1)),V).*sin(2.*pi.*x(2).*t+x(3)).^2))/n

Error in finaldenoise (line 46)

pop(i).Cost=CostFunction(pop(i).Position);

>>

[common fernando]

the orign code or the line contain minus :

CostFunction=@(x) sum((V-x(1)*sin(2*pi*x(2)*t+x(3))).^2)/n; % Cost Function

give me the error:

Undefined function 'minus' for input arguments of type 'function_handle'.

Error in @(x)sum((V-x(1)*sin(2*pi*x(2)*t+x(3))).^2)/n

Error in finaldenoise (line 46)

pop(i).Cost=CostFunction(pop(i).Position);

>>

i changed it by : @(x)sum((minus((x(1)),V).*sin(2.*pi.*x(2).*t+x(3)).^2))/n

but the same error

[Stephan]

what do you get if you enter this in the command line:

test = @(x,y) minus(x,y) * 4 + x.*y
test(2,3)

result should be:

test =

  function_handle with value:

    @(x,y)minus(x,y)*4+x.*y


ans =

     2

[common fernando]

yes answer =2

test =

@(x,y)minus(x,y)*4+x.*y

ans =

2

what do you want to tell me?

[Stephan]

This works for me:

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+n-1)*dt];
        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') 
hold on

xlabel('time')
ylabel('frequency')
title('DE white noise')

plot (t,fi)
hold off

[common fernando]

[Stephan]

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')

[common fernando]

I'll try it , thanks at all