Dear all
I am plotting the following:
u1=figure(‘visible’,’off’,’units’,’pixels’,’position’,[0 0 1920 1080]);
t=tiledlayout(2,2,’Padding’,’compact’);
title(t,[‘Bilayer. Monte Carlo, $15$ million steps. $T=2$ K, $mathbf{H}=0$’;”;”],’FontSize’,18,’interpreter’,’latex’);
ax1=nexttile;
uimagesc(space_x,space_y,mx);
axis xy;
clim([-round(max([abs(max(max(mx))) abs(min(min(mx)))]),2) round(max([abs(max(max(mx))) abs(min(min(mx)))]),2)]);
colormap(ax1,bluewhitered(256));
box on;
clr1=colorbar(‘YTick’,[-round(max([abs(max(max(mx))) abs(min(min(mx)))]),2) -round(max([abs(max(max(mx))) abs(min(min(mx)))])/2,2) 0 round(max([abs(max(max(mx))) abs(min(min(mx)))])/2,2) round(max([abs(max(max(mx))) abs(min(min(mx)))]),2)]);
set(clr1,’TickLabelInterpreter’,’latex’);
xlabel(‘$a$-{it th} spatial direction, $a , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(‘$b$-{it th} spatial direction, $b , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(clr1,’$a$-{it th} magnetization component, $m_a$’,’Interpreter’,’Latex’,’FontSize’,16);
xlim([0 300]);
xticks([0:50:300]);
ylim([0 100]);
yticks([0:20:100]);
pbaspect([1 1/3 1]);
xtickangle(0);
set(gca,’TickLabelInterpreter’,’latex’,’FontSize’,15);
ax2=nexttile;
uimagesc(space_x,space_y,my);
clim([-max([abs(max(max(my))) abs(min(min(my)))]) max([abs(max(max(my))) abs(min(min(my)))])]);
colormap(ax2,bluewhitered(256));
axis xy;
box on;
clr2=colorbar(‘YTick’,[-max([abs(max(max(my))) abs(min(min(my)))]) -max([abs(max(max(my))) abs(min(min(my)))])/2 0 max([abs(max(max(my))) abs(min(min(my)))])/2 max([abs(max(max(my))) abs(min(min(my)))])]);
set(clr2,’TickLabelInterpreter’,’latex’);
xlabel(‘$a$-{it th} spatial direction, $a , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(‘$b$-{it th} spatial direction, $b , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(clr2,’$b$-{it th} magnetization component, $m_b$’,’Interpreter’,’Latex’,’FontSize’,16);
xlim([0 300]);
xticks([0:50:300]);
ylim([0 100]);
yticks([0:20:100]);
pbaspect([1 1/3 1]);
xtickangle(0);
set(gca,’TickLabelInterpreter’,’latex’,’FontSize’,15);
ax3=nexttile;
uimagesc(space_x,space_y,mz);
box on;
axis xy;
clim([-max([abs(max(max(mz))) abs(min(min(mz)))]) max([abs(max(max(mz))) abs(min(min(mz)))])]);
colormap(ax3,bluewhitered(256));
clr3=colorbar(‘YTick’,[-max([abs(max(max(mz))) abs(min(min(mz)))]) -max([abs(max(max(mz))) abs(min(min(mz)))])/2 0 max([abs(max(max(mz))) abs(min(min(mz)))])/2 max([abs(max(max(mz))) abs(min(min(mz)))])]);
set(clr3,’TickLabelInterpreter’,’latex’);
xlabel(‘$a$-{it th} spatial direction, $a , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(‘$b$-{it th} spatial direction, $b , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(clr3,’$z$-{it th} magnetization component, $m_z$’,’Interpreter’,’Latex’,’FontSize’,16);
xlim([0 300]);
xticks([0:50:300]);
ylim([0 100]);
yticks([0:20:100]);
pbaspect([1 1/3 1]);
xtickangle(0);
set(gca,’TickLabelInterpreter’,’latex’,’FontSize’,15);
ax4=nexttile;
uimagesc(space_x,space_y,total_magnetization);
box on;
axis xy;
clim([0 max(max(total_magnetization))]);
colormap(ax4,cmap);
clr4=colorbar(‘YTick’,[0 max(max(total_magnetization))/5 2*max(max(total_magnetization))/5 3*max(max(total_magnetization))/5 4*max(max(total_magnetization))/5 max(max(total_magnetization))]);
set(clr4,’TickLabelInterpreter’,’latex’);
xlabel(‘$a$-{it th} spatial direction, $a , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(‘$b$-{it th} spatial direction, $b , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(clr4,’Magnetization vector modulus, $left| mathbf{m} right|$’,’Interpreter’,’Latex’,’FontSize’,16);
xlim([0 300]);
xticks([0:50:300]);
ylim([0 100]);
yticks([0:20:100]);
pbaspect([1 1/3 1]);
xtickangle(0);
set(gca,’TickLabelInterpreter’,’latex’,’FontSize’,15);
set(gcf,’color’,’white’);
set(gca,’Units’,’normalized’);
set(u1,’Units’,’Inches’);
posu1=get(u1,’Position’);
set(u1,’PaperPositionMode’,’Auto’,’PaperUnits’,’Inches’,’PaperSize’,[posu1(3),posu1(4)]);
and the plot looks like:

As you can see there is some vertical unnecessary space between the two horizontal stacks of plots. I would like to reduce it to be able to put the general title "Bilayer…" far enough from the top plots.
Any ideas?Dear all
I am plotting the following:
u1=figure(‘visible’,’off’,’units’,’pixels’,’position’,[0 0 1920 1080]);
t=tiledlayout(2,2,’Padding’,’compact’);
title(t,[‘Bilayer. Monte Carlo, $15$ million steps. $T=2$ K, $mathbf{H}=0$’;”;”],’FontSize’,18,’interpreter’,’latex’);
ax1=nexttile;
uimagesc(space_x,space_y,mx);
axis xy;
clim([-round(max([abs(max(max(mx))) abs(min(min(mx)))]),2) round(max([abs(max(max(mx))) abs(min(min(mx)))]),2)]);
colormap(ax1,bluewhitered(256));
box on;
clr1=colorbar(‘YTick’,[-round(max([abs(max(max(mx))) abs(min(min(mx)))]),2) -round(max([abs(max(max(mx))) abs(min(min(mx)))])/2,2) 0 round(max([abs(max(max(mx))) abs(min(min(mx)))])/2,2) round(max([abs(max(max(mx))) abs(min(min(mx)))]),2)]);
set(clr1,’TickLabelInterpreter’,’latex’);
xlabel(‘$a$-{it th} spatial direction, $a , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(‘$b$-{it th} spatial direction, $b , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(clr1,’$a$-{it th} magnetization component, $m_a$’,’Interpreter’,’Latex’,’FontSize’,16);
xlim([0 300]);
xticks([0:50:300]);
ylim([0 100]);
yticks([0:20:100]);
pbaspect([1 1/3 1]);
xtickangle(0);
set(gca,’TickLabelInterpreter’,’latex’,’FontSize’,15);
ax2=nexttile;
uimagesc(space_x,space_y,my);
clim([-max([abs(max(max(my))) abs(min(min(my)))]) max([abs(max(max(my))) abs(min(min(my)))])]);
colormap(ax2,bluewhitered(256));
axis xy;
box on;
clr2=colorbar(‘YTick’,[-max([abs(max(max(my))) abs(min(min(my)))]) -max([abs(max(max(my))) abs(min(min(my)))])/2 0 max([abs(max(max(my))) abs(min(min(my)))])/2 max([abs(max(max(my))) abs(min(min(my)))])]);
set(clr2,’TickLabelInterpreter’,’latex’);
xlabel(‘$a$-{it th} spatial direction, $a , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(‘$b$-{it th} spatial direction, $b , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(clr2,’$b$-{it th} magnetization component, $m_b$’,’Interpreter’,’Latex’,’FontSize’,16);
xlim([0 300]);
xticks([0:50:300]);
ylim([0 100]);
yticks([0:20:100]);
pbaspect([1 1/3 1]);
xtickangle(0);
set(gca,’TickLabelInterpreter’,’latex’,’FontSize’,15);
ax3=nexttile;
uimagesc(space_x,space_y,mz);
box on;
axis xy;
clim([-max([abs(max(max(mz))) abs(min(min(mz)))]) max([abs(max(max(mz))) abs(min(min(mz)))])]);
colormap(ax3,bluewhitered(256));
clr3=colorbar(‘YTick’,[-max([abs(max(max(mz))) abs(min(min(mz)))]) -max([abs(max(max(mz))) abs(min(min(mz)))])/2 0 max([abs(max(max(mz))) abs(min(min(mz)))])/2 max([abs(max(max(mz))) abs(min(min(mz)))])]);
set(clr3,’TickLabelInterpreter’,’latex’);
xlabel(‘$a$-{it th} spatial direction, $a , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(‘$b$-{it th} spatial direction, $b , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(clr3,’$z$-{it th} magnetization component, $m_z$’,’Interpreter’,’Latex’,’FontSize’,16);
xlim([0 300]);
xticks([0:50:300]);
ylim([0 100]);
yticks([0:20:100]);
pbaspect([1 1/3 1]);
xtickangle(0);
set(gca,’TickLabelInterpreter’,’latex’,’FontSize’,15);
ax4=nexttile;
uimagesc(space_x,space_y,total_magnetization);
box on;
axis xy;
clim([0 max(max(total_magnetization))]);
colormap(ax4,cmap);
clr4=colorbar(‘YTick’,[0 max(max(total_magnetization))/5 2*max(max(total_magnetization))/5 3*max(max(total_magnetization))/5 4*max(max(total_magnetization))/5 max(max(total_magnetization))]);
set(clr4,’TickLabelInterpreter’,’latex’);
xlabel(‘$a$-{it th} spatial direction, $a , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(‘$b$-{it th} spatial direction, $b , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(clr4,’Magnetization vector modulus, $left| mathbf{m} right|$’,’Interpreter’,’Latex’,’FontSize’,16);
xlim([0 300]);
xticks([0:50:300]);
ylim([0 100]);
yticks([0:20:100]);
pbaspect([1 1/3 1]);
xtickangle(0);
set(gca,’TickLabelInterpreter’,’latex’,’FontSize’,15);
set(gcf,’color’,’white’);
set(gca,’Units’,’normalized’);
set(u1,’Units’,’Inches’);
posu1=get(u1,’Position’);
set(u1,’PaperPositionMode’,’Auto’,’PaperUnits’,’Inches’,’PaperSize’,[posu1(3),posu1(4)]);
and the plot looks like:

As you can see there is some vertical unnecessary space between the two horizontal stacks of plots. I would like to reduce it to be able to put the general title "Bilayer…" far enough from the top plots.
Any ideas? Dear all
I am plotting the following:
u1=figure(‘visible’,’off’,’units’,’pixels’,’position’,[0 0 1920 1080]);
t=tiledlayout(2,2,’Padding’,’compact’);
title(t,[‘Bilayer. Monte Carlo, $15$ million steps. $T=2$ K, $mathbf{H}=0$’;”;”],’FontSize’,18,’interpreter’,’latex’);
ax1=nexttile;
uimagesc(space_x,space_y,mx);
axis xy;
clim([-round(max([abs(max(max(mx))) abs(min(min(mx)))]),2) round(max([abs(max(max(mx))) abs(min(min(mx)))]),2)]);
colormap(ax1,bluewhitered(256));
box on;
clr1=colorbar(‘YTick’,[-round(max([abs(max(max(mx))) abs(min(min(mx)))]),2) -round(max([abs(max(max(mx))) abs(min(min(mx)))])/2,2) 0 round(max([abs(max(max(mx))) abs(min(min(mx)))])/2,2) round(max([abs(max(max(mx))) abs(min(min(mx)))]),2)]);
set(clr1,’TickLabelInterpreter’,’latex’);
xlabel(‘$a$-{it th} spatial direction, $a , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(‘$b$-{it th} spatial direction, $b , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(clr1,’$a$-{it th} magnetization component, $m_a$’,’Interpreter’,’Latex’,’FontSize’,16);
xlim([0 300]);
xticks([0:50:300]);
ylim([0 100]);
yticks([0:20:100]);
pbaspect([1 1/3 1]);
xtickangle(0);
set(gca,’TickLabelInterpreter’,’latex’,’FontSize’,15);
ax2=nexttile;
uimagesc(space_x,space_y,my);
clim([-max([abs(max(max(my))) abs(min(min(my)))]) max([abs(max(max(my))) abs(min(min(my)))])]);
colormap(ax2,bluewhitered(256));
axis xy;
box on;
clr2=colorbar(‘YTick’,[-max([abs(max(max(my))) abs(min(min(my)))]) -max([abs(max(max(my))) abs(min(min(my)))])/2 0 max([abs(max(max(my))) abs(min(min(my)))])/2 max([abs(max(max(my))) abs(min(min(my)))])]);
set(clr2,’TickLabelInterpreter’,’latex’);
xlabel(‘$a$-{it th} spatial direction, $a , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(‘$b$-{it th} spatial direction, $b , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(clr2,’$b$-{it th} magnetization component, $m_b$’,’Interpreter’,’Latex’,’FontSize’,16);
xlim([0 300]);
xticks([0:50:300]);
ylim([0 100]);
yticks([0:20:100]);
pbaspect([1 1/3 1]);
xtickangle(0);
set(gca,’TickLabelInterpreter’,’latex’,’FontSize’,15);
ax3=nexttile;
uimagesc(space_x,space_y,mz);
box on;
axis xy;
clim([-max([abs(max(max(mz))) abs(min(min(mz)))]) max([abs(max(max(mz))) abs(min(min(mz)))])]);
colormap(ax3,bluewhitered(256));
clr3=colorbar(‘YTick’,[-max([abs(max(max(mz))) abs(min(min(mz)))]) -max([abs(max(max(mz))) abs(min(min(mz)))])/2 0 max([abs(max(max(mz))) abs(min(min(mz)))])/2 max([abs(max(max(mz))) abs(min(min(mz)))])]);
set(clr3,’TickLabelInterpreter’,’latex’);
xlabel(‘$a$-{it th} spatial direction, $a , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(‘$b$-{it th} spatial direction, $b , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(clr3,’$z$-{it th} magnetization component, $m_z$’,’Interpreter’,’Latex’,’FontSize’,16);
xlim([0 300]);
xticks([0:50:300]);
ylim([0 100]);
yticks([0:20:100]);
pbaspect([1 1/3 1]);
xtickangle(0);
set(gca,’TickLabelInterpreter’,’latex’,’FontSize’,15);
ax4=nexttile;
uimagesc(space_x,space_y,total_magnetization);
box on;
axis xy;
clim([0 max(max(total_magnetization))]);
colormap(ax4,cmap);
clr4=colorbar(‘YTick’,[0 max(max(total_magnetization))/5 2*max(max(total_magnetization))/5 3*max(max(total_magnetization))/5 4*max(max(total_magnetization))/5 max(max(total_magnetization))]);
set(clr4,’TickLabelInterpreter’,’latex’);
xlabel(‘$a$-{it th} spatial direction, $a , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(‘$b$-{it th} spatial direction, $b , , left( mathrm{nm} right)$’,’FontSize’,15,’interpreter’,’latex’);
ylabel(clr4,’Magnetization vector modulus, $left| mathbf{m} right|$’,’Interpreter’,’Latex’,’FontSize’,16);
xlim([0 300]);
xticks([0:50:300]);
ylim([0 100]);
yticks([0:20:100]);
pbaspect([1 1/3 1]);
xtickangle(0);
set(gca,’TickLabelInterpreter’,’latex’,’FontSize’,15);
set(gcf,’color’,’white’);
set(gca,’Units’,’normalized’);
set(u1,’Units’,’Inches’);
posu1=get(u1,’Position’);
set(u1,’PaperPositionMode’,’Auto’,’PaperUnits’,’Inches’,’PaperSize’,[posu1(3),posu1(4)]);
and the plot looks like:

As you can see there is some vertical unnecessary space between the two horizontal stacks of plots. I would like to reduce it to be able to put the general title "Bilayer…" far enough from the top plots.
Any ideas? padding compact tiledlayout MATLAB Answers — New Questions

​

I used the following repository to evaluate my data (https://github.com/matlab-deep-learning/pretrained-salsanext), but it loads a pretrained SalsaNext model. I would like to know whether it is possible to untrain the model, or to train it from scratch without creating the layergraph (Since this model involves a large number of layers, and the work seems to be cumbersome, and error-prone).

ThanksI used the following repository to evaluate my data (https://github.com/matlab-deep-learning/pretrained-salsanext), but it loads a pretrained SalsaNext model. I would like to know whether it is possible to untrain the model, or to train it from scratch without creating the layergraph (Since this model involves a large number of layers, and the work seems to be cumbersome, and error-prone).

Thanks I used the following repository to evaluate my data (https://github.com/matlab-deep-learning/pretrained-salsanext), but it loads a pretrained SalsaNext model. I would like to know whether it is possible to untrain the model, or to train it from scratch without creating the layergraph (Since this model involves a large number of layers, and the work seems to be cumbersome, and error-prone).

Thanks salsanext, untraining, neural network MATLAB Answers — New Questions

​

I am using the Synchronous Machine Model 1.0 in Simulink Simscape.
Does the rotor core of the Synchronous Machine Model 1.0 include hysteresis characteristics?I am using the Synchronous Machine Model 1.0 in Simulink Simscape.
Does the rotor core of the Synchronous Machine Model 1.0 include hysteresis characteristics? I am using the Synchronous Machine Model 1.0 in Simulink Simscape.
Does the rotor core of the Synchronous Machine Model 1.0 include hysteresis characteristics? simulink, simscape, electric_motor_control, model MATLAB Answers — New Questions

​

I want to plot the auto correlation function for 15 numbers without using the inbuilt function.
My code is as follows:

P=[ 47,64,23,71,38,64,55,41,59,48]; %%,71,35,57,40,58 ];
N=length(P);
Q=P(1:N);
R=P(1:N);

N1=length(Q);
N2=length(R);
u=mean(P);

sum= ((Q-u)*(R-u).’);
covar0=sum/N1;

for k=1:N-1 %% k is no. of lags and its max value can be 14 here
Q1=P(1:N-k); %% Splitting the series into 2 series separated by lag k
R1=P(1+k:N);
N1_1=length(Q1);
N2_1=length(R1);

sum1= ((Q1-u)*(R1-u).’);
covark=sum1/N;
acf=covark/covar0;
acf;
end

But after execution of the loop,I am getting acf value only for last value of k, which rather should have been an array for values of k=1 to 14.
I am new to matlab. Plz help me resolve the code.I want to plot the auto correlation function for 15 numbers without using the inbuilt function.
My code is as follows:

P=[ 47,64,23,71,38,64,55,41,59,48]; %%,71,35,57,40,58 ];
N=length(P);
Q=P(1:N);
R=P(1:N);

N1=length(Q);
N2=length(R);
u=mean(P);

sum= ((Q-u)*(R-u).’);
covar0=sum/N1;

for k=1:N-1 %% k is no. of lags and its max value can be 14 here
Q1=P(1:N-k); %% Splitting the series into 2 series separated by lag k
R1=P(1+k:N);
N1_1=length(Q1);
N2_1=length(R1);

sum1= ((Q1-u)*(R1-u).’);
covark=sum1/N;
acf=covark/covar0;
acf;
end

But after execution of the loop,I am getting acf value only for last value of k, which rather should have been an array for values of k=1 to 14.
I am new to matlab. Plz help me resolve the code. I want to plot the auto correlation function for 15 numbers without using the inbuilt function.
My code is as follows:

P=[ 47,64,23,71,38,64,55,41,59,48]; %%,71,35,57,40,58 ];
N=length(P);
Q=P(1:N);
R=P(1:N);

N1=length(Q);
N2=length(R);
u=mean(P);

sum= ((Q-u)*(R-u).’);
covar0=sum/N1;

for k=1:N-1 %% k is no. of lags and its max value can be 14 here
Q1=P(1:N-k); %% Splitting the series into 2 series separated by lag k
R1=P(1+k:N);
N1_1=length(Q1);
N2_1=length(R1);

sum1= ((Q1-u)*(R1-u).’);
covark=sum1/N;
acf=covark/covar0;
acf;
end

But after execution of the loop,I am getting acf value only for last value of k, which rather should have been an array for values of k=1 to 14.
I am new to matlab. Plz help me resolve the code. auto correlation, for loop MATLAB Answers — New Questions

​

Hi,
I have a problem with the visualization of Chart diagram and related properties in my model.
I using this code line (in a matlab script) to search all the subsystem object (including chart):
allsubsysObj = modelObj.find(‘-isa’,’Simulink.SubSystem’,’-or’,’-isa’,’Simulink.ModelReference’,’-depth’,1);
The script find the number of element that i’m expected (2 chart and 2 subsytem) but when i tried to put the diagram figure i obtained the inner content of the subsytem and the chart.
auxpara = CenterDiagram(Diagram(allsubsysObj(1)),R);
Cap = Text(strcat("Figure ",num2str(ChCounter),".",num2str(FigCounter),": ",allsubsysObj(1).Name," subsystem"));
Cap.Style = [{FontSize(Str.CapFontSize)},{Color(Str.CapColor)},{Bold(true)},{HAlign(Str.CapAlign)}];
FigCounter = FigCounter + 1;
subsec.add(auxpara);
subsec.add(Cap);
subsec.add(LineBreak());
How i can insert only the top level diagram of the chart?Hi,
I have a problem with the visualization of Chart diagram and related properties in my model.
I using this code line (in a matlab script) to search all the subsystem object (including chart):
allsubsysObj = modelObj.find(‘-isa’,’Simulink.SubSystem’,’-or’,’-isa’,’Simulink.ModelReference’,’-depth’,1);
The script find the number of element that i’m expected (2 chart and 2 subsytem) but when i tried to put the diagram figure i obtained the inner content of the subsytem and the chart.
auxpara = CenterDiagram(Diagram(allsubsysObj(1)),R);
Cap = Text(strcat("Figure ",num2str(ChCounter),".",num2str(FigCounter),": ",allsubsysObj(1).Name," subsystem"));
Cap.Style = [{FontSize(Str.CapFontSize)},{Color(Str.CapColor)},{Bold(true)},{HAlign(Str.CapAlign)}];
FigCounter = FigCounter + 1;
subsec.add(auxpara);
subsec.add(Cap);
subsec.add(LineBreak());
How i can insert only the top level diagram of the chart? Hi,
I have a problem with the visualization of Chart diagram and related properties in my model.
I using this code line (in a matlab script) to search all the subsystem object (including chart):
allsubsysObj = modelObj.find(‘-isa’,’Simulink.SubSystem’,’-or’,’-isa’,’Simulink.ModelReference’,’-depth’,1);
The script find the number of element that i’m expected (2 chart and 2 subsytem) but when i tried to put the diagram figure i obtained the inner content of the subsytem and the chart.
auxpara = CenterDiagram(Diagram(allsubsysObj(1)),R);
Cap = Text(strcat("Figure ",num2str(ChCounter),".",num2str(FigCounter),": ",allsubsysObj(1).Name," subsystem"));
Cap.Style = [{FontSize(Str.CapFontSize)},{Color(Str.CapColor)},{Bold(true)},{HAlign(Str.CapAlign)}];
FigCounter = FigCounter + 1;
subsec.add(auxpara);
subsec.add(Cap);
subsec.add(LineBreak());
How i can insert only the top level diagram of the chart? report generation MATLAB Answers — New Questions

​

I regularly develop VHDL code for controllers that is supposed to run on FPGAs/CPLDs and I’m looking for possibilities to co-simulate this code in a closed-loop simulation environment that mimics the behavior of the controlled system. So what I would like to have is that I can define a dyamic model in Matlab (e.g., as a state-space model or a transfer function) and to simulate this model together with the controller’s HDL implementation using the outputs of the models as stimuli for the controller and the output of the controller as input for the dynamic model. Is there any way I can accomplish this with Matlab? If so, what do I have to look for?

What I found so far is apparently called HDL verifier and seems to be pretty much what I want. However, I’m currently using Lattice MachXO2 CPLDs and Lattice diamond comes with "ModelSim Lattice FPGA Edition" as a simulator. According to this post
https://ch.mathworks.com/matlabcentral/answers/493504-which-modelsim-editions-can-i-use-for-cosimulation-with-hdl-verifier
HDL verifier requires a FLI (foreign language interface), which the Lattice Edition of ModelSim does not provide. Is there any way I can use HDL verifier, e.g., by using a different simulator?I regularly develop VHDL code for controllers that is supposed to run on FPGAs/CPLDs and I’m looking for possibilities to co-simulate this code in a closed-loop simulation environment that mimics the behavior of the controlled system. So what I would like to have is that I can define a dyamic model in Matlab (e.g., as a state-space model or a transfer function) and to simulate this model together with the controller’s HDL implementation using the outputs of the models as stimuli for the controller and the output of the controller as input for the dynamic model. Is there any way I can accomplish this with Matlab? If so, what do I have to look for?

What I found so far is apparently called HDL verifier and seems to be pretty much what I want. However, I’m currently using Lattice MachXO2 CPLDs and Lattice diamond comes with "ModelSim Lattice FPGA Edition" as a simulator. According to this post
https://ch.mathworks.com/matlabcentral/answers/493504-which-modelsim-editions-can-i-use-for-cosimulation-with-hdl-verifier
HDL verifier requires a FLI (foreign language interface), which the Lattice Edition of ModelSim does not provide. Is there any way I can use HDL verifier, e.g., by using a different simulator? I regularly develop VHDL code for controllers that is supposed to run on FPGAs/CPLDs and I’m looking for possibilities to co-simulate this code in a closed-loop simulation environment that mimics the behavior of the controlled system. So what I would like to have is that I can define a dyamic model in Matlab (e.g., as a state-space model or a transfer function) and to simulate this model together with the controller’s HDL implementation using the outputs of the models as stimuli for the controller and the output of the controller as input for the dynamic model. Is there any way I can accomplish this with Matlab? If so, what do I have to look for?

What I found so far is apparently called HDL verifier and seems to be pretty much what I want. However, I’m currently using Lattice MachXO2 CPLDs and Lattice diamond comes with "ModelSim Lattice FPGA Edition" as a simulator. According to this post
https://ch.mathworks.com/matlabcentral/answers/493504-which-modelsim-editions-can-i-use-for-cosimulation-with-hdl-verifier
HDL verifier requires a FLI (foreign language interface), which the Lattice Edition of ModelSim does not provide. Is there any way I can use HDL verifier, e.g., by using a different simulator? vhdl, hdl simulation, hdl verifier, cosimlation, fpga MATLAB Answers — New Questions

​

Hello, I’m currently working on a task to do a semantic segmentation on USG image to locate TMJ. I did my image labelling in Image Labeler App and only did one class so that the class region will be 1 and background is 0. I was about to train my model with unetLayers but it says "The value of ‘numClasses’ is invalid. Expected numClasses to be a scalar with value > 1."

I’m aware that someone asked this similar question here with an answer, but I want to ask, how to customize specifically unetLayers to accomodate single class? I see that unetLayers also has softmaxLayers but i can’t find the pixel layers. Thank you in advance!Hello, I’m currently working on a task to do a semantic segmentation on USG image to locate TMJ. I did my image labelling in Image Labeler App and only did one class so that the class region will be 1 and background is 0. I was about to train my model with unetLayers but it says "The value of ‘numClasses’ is invalid. Expected numClasses to be a scalar with value > 1."

I’m aware that someone asked this similar question here with an answer, but I want to ask, how to customize specifically unetLayers to accomodate single class? I see that unetLayers also has softmaxLayers but i can’t find the pixel layers. Thank you in advance! Hello, I’m currently working on a task to do a semantic segmentation on USG image to locate TMJ. I did my image labelling in Image Labeler App and only did one class so that the class region will be 1 and background is 0. I was about to train my model with unetLayers but it says "The value of ‘numClasses’ is invalid. Expected numClasses to be a scalar with value > 1."

I’m aware that someone asked this similar question here with an answer, but I want to ask, how to customize specifically unetLayers to accomodate single class? I see that unetLayers also has softmaxLayers but i can’t find the pixel layers. Thank you in advance! deep learning MATLAB Answers — New Questions

​

I am trying to understand how the FFT of different directions in MATLAB works to reproduce in C/C++ instead.
So far I have the following simple example in MATLAB:
clearvars; clc; close all;
%3D FFT test
Nx = 8;
Ny = 4;
Nz= 6;
Lx =16;
Ly = 6;
dx = Lx/Nx;
dy = Ly/Ny;
%———–
xi_x = (2*pi)/Lx;
yi_y = (2*pi)/Ly;
xi = ((0:Nx-1)/Nx)*(2*pi);
yi = ((0:Ny-1)/Ny)*(2*pi);
x = xi/xi_x;
y = yi/yi_y;

zlow = 0; %a
zupp =6; %b
Lz = (zupp-zlow);
eta_zgl = 2/Lz;
[D,zgl] = cheb(Nz);
zgl = (1/2)*(((zupp-zlow)*zgl) + (zupp+zlow));
[X,Z,Y] = meshgrid(x,zgl,y); %this gives 3d grid with z-by-x-by-y size (i.e. ZXY)
%ICs
A = 2*pi / Lx;
B = 2*pi / Ly;
u = (Z-zlow) .* (Z-zupp) .* sin(A*X).* sin(B*Y);
uh1 =(fft(u,[],3));%ZXY
uh2 =(fft(u,[],1));%ZXY
uh3 =(fft(u,[],2));%ZXY
So, in C/C++ I have a 3D tensor with (Nz+1) rows and Nx coumns and Ny matrices and taking the 1D FFT along each "row" of u returns the same results as the following in MATLAB:
uh3 =(fft(u,[],2));%ZXY
While taking the 1D FFT of u along each column of u in C/C++ returns the same result as the following in MATLAB:
uh2 =(fft(u,[],1));%ZXY
Then my question is what does this 1D FFT represent? and how should I represent it in C/C++?
uh1 =(fft(u,[],3));%ZXY
The cheb(N) function is:
function [ D, x ] = cheb ( N )
if ( N == 0 )
D = 0.0;
x = 1.0;
return
end
x = cos ( pi * ( 0 : N ) / N )’;
c = [ 2.0; ones(N-1,1); 2.0 ] .* (-1.0).^(0:N)’;
X = repmat ( x, 1, N + 1 );
dX = X – X’;

% Set the off diagonal entries.
D =( c * (1.0 ./ c )’ ) ./ ( dX + ( eye ( N + 1 ) ) );
% Diagonal entries.
D = D – diag ( sum ( D’ ) );

return
endI am trying to understand how the FFT of different directions in MATLAB works to reproduce in C/C++ instead.
So far I have the following simple example in MATLAB:
clearvars; clc; close all;
%3D FFT test
Nx = 8;
Ny = 4;
Nz= 6;
Lx =16;
Ly = 6;
dx = Lx/Nx;
dy = Ly/Ny;
%———–
xi_x = (2*pi)/Lx;
yi_y = (2*pi)/Ly;
xi = ((0:Nx-1)/Nx)*(2*pi);
yi = ((0:Ny-1)/Ny)*(2*pi);
x = xi/xi_x;
y = yi/yi_y;

zlow = 0; %a
zupp =6; %b
Lz = (zupp-zlow);
eta_zgl = 2/Lz;
[D,zgl] = cheb(Nz);
zgl = (1/2)*(((zupp-zlow)*zgl) + (zupp+zlow));
[X,Z,Y] = meshgrid(x,zgl,y); %this gives 3d grid with z-by-x-by-y size (i.e. ZXY)
%ICs
A = 2*pi / Lx;
B = 2*pi / Ly;
u = (Z-zlow) .* (Z-zupp) .* sin(A*X).* sin(B*Y);
uh1 =(fft(u,[],3));%ZXY
uh2 =(fft(u,[],1));%ZXY
uh3 =(fft(u,[],2));%ZXY
So, in C/C++ I have a 3D tensor with (Nz+1) rows and Nx coumns and Ny matrices and taking the 1D FFT along each "row" of u returns the same results as the following in MATLAB:
uh3 =(fft(u,[],2));%ZXY
While taking the 1D FFT of u along each column of u in C/C++ returns the same result as the following in MATLAB:
uh2 =(fft(u,[],1));%ZXY
Then my question is what does this 1D FFT represent? and how should I represent it in C/C++?
uh1 =(fft(u,[],3));%ZXY
The cheb(N) function is:
function [ D, x ] = cheb ( N )
if ( N == 0 )
D = 0.0;
x = 1.0;
return
end
x = cos ( pi * ( 0 : N ) / N )’;
c = [ 2.0; ones(N-1,1); 2.0 ] .* (-1.0).^(0:N)’;
X = repmat ( x, 1, N + 1 );
dX = X – X’;

% Set the off diagonal entries.
D =( c * (1.0 ./ c )’ ) ./ ( dX + ( eye ( N + 1 ) ) );
% Diagonal entries.
D = D – diag ( sum ( D’ ) );

return
end I am trying to understand how the FFT of different directions in MATLAB works to reproduce in C/C++ instead.
So far I have the following simple example in MATLAB:
clearvars; clc; close all;
%3D FFT test
Nx = 8;
Ny = 4;
Nz= 6;
Lx =16;
Ly = 6;
dx = Lx/Nx;
dy = Ly/Ny;
%———–
xi_x = (2*pi)/Lx;
yi_y = (2*pi)/Ly;
xi = ((0:Nx-1)/Nx)*(2*pi);
yi = ((0:Ny-1)/Ny)*(2*pi);
x = xi/xi_x;
y = yi/yi_y;

zlow = 0; %a
zupp =6; %b
Lz = (zupp-zlow);
eta_zgl = 2/Lz;
[D,zgl] = cheb(Nz);
zgl = (1/2)*(((zupp-zlow)*zgl) + (zupp+zlow));
[X,Z,Y] = meshgrid(x,zgl,y); %this gives 3d grid with z-by-x-by-y size (i.e. ZXY)
%ICs
A = 2*pi / Lx;
B = 2*pi / Ly;
u = (Z-zlow) .* (Z-zupp) .* sin(A*X).* sin(B*Y);
uh1 =(fft(u,[],3));%ZXY
uh2 =(fft(u,[],1));%ZXY
uh3 =(fft(u,[],2));%ZXY
So, in C/C++ I have a 3D tensor with (Nz+1) rows and Nx coumns and Ny matrices and taking the 1D FFT along each "row" of u returns the same results as the following in MATLAB:
uh3 =(fft(u,[],2));%ZXY
While taking the 1D FFT of u along each column of u in C/C++ returns the same result as the following in MATLAB:
uh2 =(fft(u,[],1));%ZXY
Then my question is what does this 1D FFT represent? and how should I represent it in C/C++?
uh1 =(fft(u,[],3));%ZXY
The cheb(N) function is:
function [ D, x ] = cheb ( N )
if ( N == 0 )
D = 0.0;
x = 1.0;
return
end
x = cos ( pi * ( 0 : N ) / N )’;
c = [ 2.0; ones(N-1,1); 2.0 ] .* (-1.0).^(0:N)’;
X = repmat ( x, 1, N + 1 );
dX = X – X’;

% Set the off diagonal entries.
D =( c * (1.0 ./ c )’ ) ./ ( dX + ( eye ( N + 1 ) ) );
% Diagonal entries.
D = D – diag ( sum ( D’ ) );

return
end meshgrid, fft, numerical libraries, c/c++ MATLAB Answers — New Questions

​

I am facing below error while running MA check ‘Check Stateflow charts for strong data typing’.
Error: Abnormal exist: Invalid or deleted object
Attaching screenshot for reference. Please help to solve this.I am facing below error while running MA check ‘Check Stateflow charts for strong data typing’.
Error: Abnormal exist: Invalid or deleted object
Attaching screenshot for reference. Please help to solve this. I am facing below error while running MA check ‘Check Stateflow charts for strong data typing’.
Error: Abnormal exist: Invalid or deleted object
Attaching screenshot for reference. Please help to solve this. model advisor, check stateflow charts for strong data typing MATLAB Answers — New Questions

​

I have simulated a system in Simulink MATLAB that I want to optimize with the PID controller.
To find the best controller coefficients, I use WOA optimization method. But when I run it, I get the following error “Derivative of state ‘1’ in block ‘two_area_LFC_PID/Integrator9’ at time 8.820174943555763 is not finite. The simulation will be stopped. There may be a singularity in the solution. If not, try reducing the step size (either by reducing the fixed step size or by tightening the error tolerances)”. Is there a solution to my problem? I have changed the step size. I also changed the solver to ODE 15s(stiff/NDF). But the problem was not solved. I have posted the desired system image with algorithm codes for friends.
CODE:
function bestSolution = EWOA_Run_PID()
% Step 1: Initialize the input arguments for EWOA
nVar = 6;
ub = [50 50 50 50 50 50];
lb = [0.01 0.01 0.01 0.01 0.01 0.01];
MaxIter = 50;
PopulationSize = 50;
% Step 2: Define the objective function handle using EWAO_Cost_PID_CF_CE
objFcn = @(params) EWOA_Cost_PID(params);
% Step 3: Call the EWOA function
bestSolution = EWOA(nVar, lb, ub, objFcn, MaxIter, PopulationSize);
end
function bestSolution = EWOA(nVar, lb, ub, objFcn, MaxIter, PopulationSize)
% Step 1: Initialize the population
whalesPositions = bsxfun(@plus, lb, bsxfun(@times, rand(PopulationSize, nVar), (ub – lb)));

% Step 2: Evaluate the objective function for each whale
whalesCost = zeros(PopulationSize, 1);
for i = 1:PopulationSize
whalesCost(i) = objFcn(whalesPositions(i, :));
end

% Step 3: Set the initial best position and cost
[globalBestCost, gBestIndex] = min(whalesCost);
globalBestPosition = whalesPositions(gBestIndex, :);

% Step 4: Initialize elite whale
eliteWhalePosition = globalBestPosition;
eliteWhaleCost = globalBestCost;

% Step 5: Set the termination criterion parameters
maxIterWithoutImprovement = 50; % Adjust this value as needed
iterWithoutImprovement = 50;
% Main loop
for iter = 1:MaxIter
a = 2 – iter * ((2) / MaxIter); % Parameter to control the search space reduction

for i = 1:PopulationSize
% Generate random vectors
r1 = rand(1, nVar);
r2 = rand(1, nVar);

% Calculate the A and C coefficients
A = 2 * a * r1 – a;
C = 2 * r2;

% Update the position of the whales using the WOA operators
D = abs(C .* globalBestPosition – whalesPositions(i, :));
newWhalePosition = globalBestPosition – A .* D;

% Clip positions to stay within the bounds
newWhalePosition = max(newWhalePosition, lb);
newWhalePosition = min(newWhalePosition, ub);

% Evaluate the new position using the EWAO_Cost_PID_CF_CE function
newWhaleCost = EWOA_Cost_PID(newWhalePosition);

% Update the whale’s position and cost if a better solution is found
if newWhaleCost < whalesCost(i)
whalesPositions(i, 🙂 = newWhalePosition;
whalesCost(i) = newWhaleCost;
end

% Update the global best position and cost if a better solution is found
if whalesCost(i) < globalBestCost
globalBestCost = whalesCost(i);
globalBestPosition = whalesPositions(i, :);
end
end

% Check for termination criterion
if globalBestCost < eliteWhaleCost
eliteWhalePosition = globalBestPosition;
eliteWhaleCost = globalBestCost;
iterWithoutImprovement = 0;
else
iterWithoutImprovement = iterWithoutImprovement + 1;
end

% Display the best cost at each iteration
disp([‘Iteration ‘, num2str(iter), ‘: Best Cost = ‘, num2str(eliteWhaleCost)]);

% Check if termination criterion is met
if iterWithoutImprovement >= maxIterWithoutImprovement
break;
end
end

% Output the best solution found
bestSolution = eliteWhalePosition;
end
Code
function cost = EWOA_Cost_PID(params)
% Assign parameters to the base workspace using assignin
assignin(‘base’, ‘KP’, params(1));
assignin(‘base’, ‘KI’, params(2));
assignin(‘base’, ‘KD’, params(3));
assignin(‘base’, ‘KPP’, params(4));
assignin(‘base’, ‘KII’, params(5));
assignin(‘base’, ‘KDD’, params(6));
% Run the Simulink model
sim(‘two_area_LFC_PID’);
% Calculate penalties based on maximum absolute values of signals
g1 = max(abs(f1.signals.values)) – 0.25;
g2 = max(abs(f2.signals.values)) – 0.25;
g3 = max(abs(pt.signals.values)) – 0.05;
% Define a large penalty coefficient
lambda = 10^20;
% Compute penalties for constraint violations
Penalty1 = lambda * g1^2 * GetInequality(g1);
Penalty2 = lambda * g2^2 * GetInequality(g2);
Penalty3 = lambda * g3^2 * GetInequality(g3);
% Calculate the total cost
cost = J_obj.signals.values(end) + Penalty1 + Penalty2 + Penalty3;
endI have simulated a system in Simulink MATLAB that I want to optimize with the PID controller.
To find the best controller coefficients, I use WOA optimization method. But when I run it, I get the following error “Derivative of state ‘1’ in block ‘two_area_LFC_PID/Integrator9’ at time 8.820174943555763 is not finite. The simulation will be stopped. There may be a singularity in the solution. If not, try reducing the step size (either by reducing the fixed step size or by tightening the error tolerances)”. Is there a solution to my problem? I have changed the step size. I also changed the solver to ODE 15s(stiff/NDF). But the problem was not solved. I have posted the desired system image with algorithm codes for friends.
CODE:
function bestSolution = EWOA_Run_PID()
% Step 1: Initialize the input arguments for EWOA
nVar = 6;
ub = [50 50 50 50 50 50];
lb = [0.01 0.01 0.01 0.01 0.01 0.01];
MaxIter = 50;
PopulationSize = 50;
% Step 2: Define the objective function handle using EWAO_Cost_PID_CF_CE
objFcn = @(params) EWOA_Cost_PID(params);
% Step 3: Call the EWOA function
bestSolution = EWOA(nVar, lb, ub, objFcn, MaxIter, PopulationSize);
end
function bestSolution = EWOA(nVar, lb, ub, objFcn, MaxIter, PopulationSize)
% Step 1: Initialize the population
whalesPositions = bsxfun(@plus, lb, bsxfun(@times, rand(PopulationSize, nVar), (ub – lb)));

% Step 2: Evaluate the objective function for each whale
whalesCost = zeros(PopulationSize, 1);
for i = 1:PopulationSize
whalesCost(i) = objFcn(whalesPositions(i, :));
end

% Step 3: Set the initial best position and cost
[globalBestCost, gBestIndex] = min(whalesCost);
globalBestPosition = whalesPositions(gBestIndex, :);

% Step 4: Initialize elite whale
eliteWhalePosition = globalBestPosition;
eliteWhaleCost = globalBestCost;

% Step 5: Set the termination criterion parameters
maxIterWithoutImprovement = 50; % Adjust this value as needed
iterWithoutImprovement = 50;
% Main loop
for iter = 1:MaxIter
a = 2 – iter * ((2) / MaxIter); % Parameter to control the search space reduction

for i = 1:PopulationSize
% Generate random vectors
r1 = rand(1, nVar);
r2 = rand(1, nVar);

% Calculate the A and C coefficients
A = 2 * a * r1 – a;
C = 2 * r2;

% Update the position of the whales using the WOA operators
D = abs(C .* globalBestPosition – whalesPositions(i, :));
newWhalePosition = globalBestPosition – A .* D;

% Clip positions to stay within the bounds
newWhalePosition = max(newWhalePosition, lb);
newWhalePosition = min(newWhalePosition, ub);

% Evaluate the new position using the EWAO_Cost_PID_CF_CE function
newWhaleCost = EWOA_Cost_PID(newWhalePosition);

% Update the whale’s position and cost if a better solution is found
if newWhaleCost < whalesCost(i)
whalesPositions(i, 🙂 = newWhalePosition;
whalesCost(i) = newWhaleCost;
end

% Update the global best position and cost if a better solution is found
if whalesCost(i) < globalBestCost
globalBestCost = whalesCost(i);
globalBestPosition = whalesPositions(i, :);
end
end

% Check for termination criterion
if globalBestCost < eliteWhaleCost
eliteWhalePosition = globalBestPosition;
eliteWhaleCost = globalBestCost;
iterWithoutImprovement = 0;
else
iterWithoutImprovement = iterWithoutImprovement + 1;
end

% Display the best cost at each iteration
disp([‘Iteration ‘, num2str(iter), ‘: Best Cost = ‘, num2str(eliteWhaleCost)]);

% Check if termination criterion is met
if iterWithoutImprovement >= maxIterWithoutImprovement
break;
end
end

% Output the best solution found
bestSolution = eliteWhalePosition;
end
Code
function cost = EWOA_Cost_PID(params)
% Assign parameters to the base workspace using assignin
assignin(‘base’, ‘KP’, params(1));
assignin(‘base’, ‘KI’, params(2));
assignin(‘base’, ‘KD’, params(3));
assignin(‘base’, ‘KPP’, params(4));
assignin(‘base’, ‘KII’, params(5));
assignin(‘base’, ‘KDD’, params(6));
% Run the Simulink model
sim(‘two_area_LFC_PID’);
% Calculate penalties based on maximum absolute values of signals
g1 = max(abs(f1.signals.values)) – 0.25;
g2 = max(abs(f2.signals.values)) – 0.25;
g3 = max(abs(pt.signals.values)) – 0.05;
% Define a large penalty coefficient
lambda = 10^20;
% Compute penalties for constraint violations
Penalty1 = lambda * g1^2 * GetInequality(g1);
Penalty2 = lambda * g2^2 * GetInequality(g2);
Penalty3 = lambda * g3^2 * GetInequality(g3);
% Calculate the total cost
cost = J_obj.signals.values(end) + Penalty1 + Penalty2 + Penalty3;
end I have simulated a system in Simulink MATLAB that I want to optimize with the PID controller.
To find the best controller coefficients, I use WOA optimization method. But when I run it, I get the following error “Derivative of state ‘1’ in block ‘two_area_LFC_PID/Integrator9’ at time 8.820174943555763 is not finite. The simulation will be stopped. There may be a singularity in the solution. If not, try reducing the step size (either by reducing the fixed step size or by tightening the error tolerances)”. Is there a solution to my problem? I have changed the step size. I also changed the solver to ODE 15s(stiff/NDF). But the problem was not solved. I have posted the desired system image with algorithm codes for friends.
CODE:
function bestSolution = EWOA_Run_PID()
% Step 1: Initialize the input arguments for EWOA
nVar = 6;
ub = [50 50 50 50 50 50];
lb = [0.01 0.01 0.01 0.01 0.01 0.01];
MaxIter = 50;
PopulationSize = 50;
% Step 2: Define the objective function handle using EWAO_Cost_PID_CF_CE
objFcn = @(params) EWOA_Cost_PID(params);
% Step 3: Call the EWOA function
bestSolution = EWOA(nVar, lb, ub, objFcn, MaxIter, PopulationSize);
end
function bestSolution = EWOA(nVar, lb, ub, objFcn, MaxIter, PopulationSize)
% Step 1: Initialize the population
whalesPositions = bsxfun(@plus, lb, bsxfun(@times, rand(PopulationSize, nVar), (ub – lb)));

% Step 2: Evaluate the objective function for each whale
whalesCost = zeros(PopulationSize, 1);
for i = 1:PopulationSize
whalesCost(i) = objFcn(whalesPositions(i, :));
end

% Step 3: Set the initial best position and cost
[globalBestCost, gBestIndex] = min(whalesCost);
globalBestPosition = whalesPositions(gBestIndex, :);

% Step 4: Initialize elite whale
eliteWhalePosition = globalBestPosition;
eliteWhaleCost = globalBestCost;

% Step 5: Set the termination criterion parameters
maxIterWithoutImprovement = 50; % Adjust this value as needed
iterWithoutImprovement = 50;
% Main loop
for iter = 1:MaxIter
a = 2 – iter * ((2) / MaxIter); % Parameter to control the search space reduction

for i = 1:PopulationSize
% Generate random vectors
r1 = rand(1, nVar);
r2 = rand(1, nVar);

% Calculate the A and C coefficients
A = 2 * a * r1 – a;
C = 2 * r2;

% Update the position of the whales using the WOA operators
D = abs(C .* globalBestPosition – whalesPositions(i, :));
newWhalePosition = globalBestPosition – A .* D;

% Clip positions to stay within the bounds
newWhalePosition = max(newWhalePosition, lb);
newWhalePosition = min(newWhalePosition, ub);

% Evaluate the new position using the EWAO_Cost_PID_CF_CE function
newWhaleCost = EWOA_Cost_PID(newWhalePosition);

% Update the whale’s position and cost if a better solution is found
if newWhaleCost < whalesCost(i)
whalesPositions(i, 🙂 = newWhalePosition;
whalesCost(i) = newWhaleCost;
end

% Update the global best position and cost if a better solution is found
if whalesCost(i) < globalBestCost
globalBestCost = whalesCost(i);
globalBestPosition = whalesPositions(i, :);
end
end

% Check for termination criterion
if globalBestCost < eliteWhaleCost
eliteWhalePosition = globalBestPosition;
eliteWhaleCost = globalBestCost;
iterWithoutImprovement = 0;
else
iterWithoutImprovement = iterWithoutImprovement + 1;
end

% Display the best cost at each iteration
disp([‘Iteration ‘, num2str(iter), ‘: Best Cost = ‘, num2str(eliteWhaleCost)]);

% Check if termination criterion is met
if iterWithoutImprovement >= maxIterWithoutImprovement
break;
end
end

% Output the best solution found
bestSolution = eliteWhalePosition;
end
Code
function cost = EWOA_Cost_PID(params)
% Assign parameters to the base workspace using assignin
assignin(‘base’, ‘KP’, params(1));
assignin(‘base’, ‘KI’, params(2));
assignin(‘base’, ‘KD’, params(3));
assignin(‘base’, ‘KPP’, params(4));
assignin(‘base’, ‘KII’, params(5));
assignin(‘base’, ‘KDD’, params(6));
% Run the Simulink model
sim(‘two_area_LFC_PID’);
% Calculate penalties based on maximum absolute values of signals
g1 = max(abs(f1.signals.values)) – 0.25;
g2 = max(abs(f2.signals.values)) – 0.25;
g3 = max(abs(pt.signals.values)) – 0.05;
% Define a large penalty coefficient
lambda = 10^20;
% Compute penalties for constraint violations
Penalty1 = lambda * g1^2 * GetInequality(g1);
Penalty2 = lambda * g2^2 * GetInequality(g2);
Penalty3 = lambda * g3^2 * GetInequality(g3);
% Calculate the total cost
cost = J_obj.signals.values(end) + Penalty1 + Penalty2 + Penalty3;
end error, matlab, solve, derivative of state ‘1’ in block MATLAB Answers — New Questions

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