I want to create a matrix that the main diagonal of K are alternatively 2 and -2’s, the sub- and sup-diagonal of K alternatively
1 and -1’s, and everywhere else 0. The size of K is 2n by 2n.

Here is what I got so far.

x=ones(1,5);
y=ones(1,4);
x2=2*x;
y2=y*-1;
z=diag(x2,0)
[rows, columns] = size(z)

z(1:2*rows+2:end) = -z(1:2*rows+2:end)

b=diag(y2,+1)

d=diag(y2,-1)
g=z+b+dI want to create a matrix that the main diagonal of K are alternatively 2 and -2’s, the sub- and sup-diagonal of K alternatively
1 and -1’s, and everywhere else 0. The size of K is 2n by 2n.

Here is what I got so far.

x=ones(1,5);
y=ones(1,4);
x2=2*x;
y2=y*-1;
z=diag(x2,0)
[rows, columns] = size(z)

z(1:2*rows+2:end) = -z(1:2*rows+2:end)

b=diag(y2,+1)

d=diag(y2,-1)
g=z+b+d I want to create a matrix that the main diagonal of K are alternatively 2 and -2’s, the sub- and sup-diagonal of K alternatively
1 and -1’s, and everywhere else 0. The size of K is 2n by 2n.

Here is what I got so far.

x=ones(1,5);
y=ones(1,4);
x2=2*x;
y2=y*-1;
z=diag(x2,0)
[rows, columns] = size(z)

z(1:2*rows+2:end) = -z(1:2*rows+2:end)

b=diag(y2,+1)

d=diag(y2,-1)
g=z+b+d matrix manipulation MATLAB Answers — New Questions

​

I’m using an API from an Audio Precision to detect the available channels from an ASIO device. The API outputs a .NET array of type IChannelInfo[] that I can’t seem to get information from. If I create the array as chanInfo and try to access the first object either with chanInfo(1) or chanInfo.GetValue(1) or any other number of Methods, it just returns a "MarshalByRefObject" with no properties that I can’t figure out how to actually use. I would expect it to either just return a string like "Analog 2 (1)" or to return another .NET object that includes the name, channel number, etc.I’m using an API from an Audio Precision to detect the available channels from an ASIO device. The API outputs a .NET array of type IChannelInfo[] that I can’t seem to get information from. If I create the array as chanInfo and try to access the first object either with chanInfo(1) or chanInfo.GetValue(1) or any other number of Methods, it just returns a "MarshalByRefObject" with no properties that I can’t figure out how to actually use. I would expect it to either just return a string like "Analog 2 (1)" or to return another .NET object that includes the name, channel number, etc. I’m using an API from an Audio Precision to detect the available channels from an ASIO device. The API outputs a .NET array of type IChannelInfo[] that I can’t seem to get information from. If I create the array as chanInfo and try to access the first object either with chanInfo(1) or chanInfo.GetValue(1) or any other number of Methods, it just returns a "MarshalByRefObject" with no properties that I can’t figure out how to actually use. I would expect it to either just return a string like "Analog 2 (1)" or to return another .NET object that includes the name, channel number, etc. .net, system MATLAB Answers — New Questions

​

I am using tiled layout(‘flow’) to plot a series of 20 plots on one figure. I want to have just one x and one y label for the entire figure, but when I try using the following code I get a figure that plots every subplot over each other.
figure()
t=tiledlayout(‘flow’);
autoCorr = zeros(ceil(length(dataSource)),N);
lag = zeros(ceil(length(dataSource)),N);
for ii=1:N
[autoCorr(:,ii),lag(:,ii)] = autocorrelation(dataSource(:,ii));
nexttile(t)
plot(lag(:,ii),autoCorr(:,ii))
end
title(t,’title’)
xlabel(t,’xlabel’)
ylabel(t,’ylabel’)

When I use this code the plot works fine but I’m not sure how to add the x and y axis labels.
autoCorr = zeros(ceil(length(dataSource)),N);
lag = zeros(ceil(length(dataSource)),N);
figure()
tiledlayout(‘flow’)
for ii=1:N
[autoCorr(:,ii),lag(:,ii)] = autocorrelation(dataSource(:,ii));
nexttile
plot(lag(:,ii),autoCorr(:,ii))
% xlim([100,fmax])
endI am using tiled layout(‘flow’) to plot a series of 20 plots on one figure. I want to have just one x and one y label for the entire figure, but when I try using the following code I get a figure that plots every subplot over each other.
figure()
t=tiledlayout(‘flow’);
autoCorr = zeros(ceil(length(dataSource)),N);
lag = zeros(ceil(length(dataSource)),N);
for ii=1:N
[autoCorr(:,ii),lag(:,ii)] = autocorrelation(dataSource(:,ii));
nexttile(t)
plot(lag(:,ii),autoCorr(:,ii))
end
title(t,’title’)
xlabel(t,’xlabel’)
ylabel(t,’ylabel’)

When I use this code the plot works fine but I’m not sure how to add the x and y axis labels.
autoCorr = zeros(ceil(length(dataSource)),N);
lag = zeros(ceil(length(dataSource)),N);
figure()
tiledlayout(‘flow’)
for ii=1:N
[autoCorr(:,ii),lag(:,ii)] = autocorrelation(dataSource(:,ii));
nexttile
plot(lag(:,ii),autoCorr(:,ii))
% xlim([100,fmax])
end I am using tiled layout(‘flow’) to plot a series of 20 plots on one figure. I want to have just one x and one y label for the entire figure, but when I try using the following code I get a figure that plots every subplot over each other.
figure()
t=tiledlayout(‘flow’);
autoCorr = zeros(ceil(length(dataSource)),N);
lag = zeros(ceil(length(dataSource)),N);
for ii=1:N
[autoCorr(:,ii),lag(:,ii)] = autocorrelation(dataSource(:,ii));
nexttile(t)
plot(lag(:,ii),autoCorr(:,ii))
end
title(t,’title’)
xlabel(t,’xlabel’)
ylabel(t,’ylabel’)

When I use this code the plot works fine but I’m not sure how to add the x and y axis labels.
autoCorr = zeros(ceil(length(dataSource)),N);
lag = zeros(ceil(length(dataSource)),N);
figure()
tiledlayout(‘flow’)
for ii=1:N
[autoCorr(:,ii),lag(:,ii)] = autocorrelation(dataSource(:,ii));
nexttile
plot(lag(:,ii),autoCorr(:,ii))
% xlim([100,fmax])
end tiled layout, tiledlayout, tiles, plot, subplot MATLAB Answers — New Questions

​

Hello dear Community,

i am currently trying to use a Feed-Forward Neural Network for a regression problem.
The input data i want to feed in are "rotational speed", "torque" and "time".
The target data is the temperature of an electrical drive.
And then i will have different operating points dependend on speed:
2000 rpm, 4000 rpm ….
The torque and time however stays the same for each point.
Right now, i am using a sequenceInputLayer and arranged the Data in 4 cells, where each cell contains 3 columns of data. One cell represents one OP.
The results are not good, and my gut tells me, that i might have a misunderstanding the way i treat the input data.

I am using MATLAB r24a and am training the network with the "trainnet"-function.
If you have any Suggestions to do this in a different way, go ahead.
Thank you very much in advance.Hello dear Community,

i am currently trying to use a Feed-Forward Neural Network for a regression problem.
The input data i want to feed in are "rotational speed", "torque" and "time".
The target data is the temperature of an electrical drive.
And then i will have different operating points dependend on speed:
2000 rpm, 4000 rpm ….
The torque and time however stays the same for each point.
Right now, i am using a sequenceInputLayer and arranged the Data in 4 cells, where each cell contains 3 columns of data. One cell represents one OP.
The results are not good, and my gut tells me, that i might have a misunderstanding the way i treat the input data.

I am using MATLAB r24a and am training the network with the "trainnet"-function.
If you have any Suggestions to do this in a different way, go ahead.
Thank you very much in advance. Hello dear Community,

i am currently trying to use a Feed-Forward Neural Network for a regression problem.
The input data i want to feed in are "rotational speed", "torque" and "time".
The target data is the temperature of an electrical drive.
And then i will have different operating points dependend on speed:
2000 rpm, 4000 rpm ….
The torque and time however stays the same for each point.
Right now, i am using a sequenceInputLayer and arranged the Data in 4 cells, where each cell contains 3 columns of data. One cell represents one OP.
The results are not good, and my gut tells me, that i might have a misunderstanding the way i treat the input data.

I am using MATLAB r24a and am training the network with the "trainnet"-function.
If you have any Suggestions to do this in a different way, go ahead.
Thank you very much in advance. deep learning, neural network, regression, data MATLAB Answers — New Questions

​

Please refer to the Matlab example (https://www.mathworks.com/help/images/learning-to-see-in-the-dark.html), This page itself acknowledges that "The colors of the network prediction are less saturated and vibrant than in the ground truth". I have compared it against the original TF model as provided by the original authors (https://github.com/cchen156/Learning-to-See-in-the-Dark) which DOES provide a very good reconstruction of the ground truth with vibrant colors. I have tried retraining the model in Matlab and got similarly poor results as the Matlab-provided pretrained model. Could someone please throw light on what could be causing this? I am afraid without a solution, Matlab example is not useful at all compared to the Tensorflow solution.Please refer to the Matlab example (https://www.mathworks.com/help/images/learning-to-see-in-the-dark.html), This page itself acknowledges that "The colors of the network prediction are less saturated and vibrant than in the ground truth". I have compared it against the original TF model as provided by the original authors (https://github.com/cchen156/Learning-to-See-in-the-Dark) which DOES provide a very good reconstruction of the ground truth with vibrant colors. I have tried retraining the model in Matlab and got similarly poor results as the Matlab-provided pretrained model. Could someone please throw light on what could be causing this? I am afraid without a solution, Matlab example is not useful at all compared to the Tensorflow solution. Please refer to the Matlab example (https://www.mathworks.com/help/images/learning-to-see-in-the-dark.html), This page itself acknowledges that "The colors of the network prediction are less saturated and vibrant than in the ground truth". I have compared it against the original TF model as provided by the original authors (https://github.com/cchen156/Learning-to-See-in-the-Dark) which DOES provide a very good reconstruction of the ground truth with vibrant colors. I have tried retraining the model in Matlab and got similarly poor results as the Matlab-provided pretrained model. Could someone please throw light on what could be causing this? I am afraid without a solution, Matlab example is not useful at all compared to the Tensorflow solution. see in the light dataset, image regression, low light enhancement MATLAB Answers — New Questions

​

Why this error, Not able to highlight because model changed error during Coverage analysis

With every model I used.Why this error, Not able to highlight because model changed error during Coverage analysis

With every model I used. Why this error, Not able to highlight because model changed error during Coverage analysis

With every model I used. coverage, modeling properties, simulink MATLAB Answers — New Questions

​

I am post-processing Abaqus data on Matlab. Overall the data looks good, however, in some areas it seems to be jiggly.

I’ve looked up for the causes, but couldn’t.

Help me please.I am post-processing Abaqus data on Matlab. Overall the data looks good, however, in some areas it seems to be jiggly.

I’ve looked up for the causes, but couldn’t.

Help me please. I am post-processing Abaqus data on Matlab. Overall the data looks good, however, in some areas it seems to be jiggly.

I’ve looked up for the causes, but couldn’t.

Help me please. postprocessing MATLAB Answers — New Questions

​

Hi:

I’m having a hard time understanding the difference between the PID block and the "manual" PID. For better understanding see the enclosed image. The gains are the same for both systems (same transfer function) but for some reason the result is not the same. What could be wrong?

<</matlabcentral/answers/uploaded_files/6343/Question.png>>Hi:

I’m having a hard time understanding the difference between the PID block and the "manual" PID. For better understanding see the enclosed image. The gains are the same for both systems (same transfer function) but for some reason the result is not the same. What could be wrong?

<</matlabcentral/answers/uploaded_files/6343/Question.png>> Hi:

I’m having a hard time understanding the difference between the PID block and the "manual" PID. For better understanding see the enclosed image. The gains are the same for both systems (same transfer function) but for some reason the result is not the same. What could be wrong?

<</matlabcentral/answers/uploaded_files/6343/Question.png>> simulink, pid MATLAB Answers — New Questions

​

Hi everybody!
I’m trying to solve a fitting curve problem concerning a double-exponential fit.
I have this data set (Data.txt) that behaves like a bi-exponential function following a resting condition. Therefore, since x<0 the signal is almost steady whereas, for x>0 the signal presents a first exponential phase (lasting about 20 sec) followed by a second exponential phase.
The problem is that the resulting fitting does not model the signal behavior in the correct way. Specifically, the first exp phase is too short and/or too low even though correct StartingPoint and Upper and Lower limits are set.
Lastly, I’ve written two algorithms with and without "Exclude" options. The second one presents a strange pattern at the onset of the second exp function.
I’ll appreciate any suggestions.

This is the code.

[x, y] = readvars(‘prova.txt’);

tRest = abs(min(x)); % Resting phase duration (sec)
cdp = 20; % estimated duration (sec) of CardioDynamicPhase (cdp)
cdpEpoch = 5; % epoch duration (sec) to compute mean value of amplitude of cdp
plot(x,y);

% Fitting StartingPoint
yRest = mean( y(1:tRest-1) ); % y value at resting condition before exp onset

A1start = mean( y(1:tRest+cdp-cdpEpoch):y(tRest+cdp) )-yRest; % mean y value of the last 5sec of the cardiodynamic phase
TD1start = 0; % starting value (sec) of the first exponential phase onset
tau1Start = 8; % starting value (sec) of the first exponential phase time constant

A2start = max(y)-A1start-yRest; % starting value of the second exponential phase amplitude
TD2start = 30; % starting value (sec) of the second exponential phase onset
tau2Start = 30; % starting value (sec) of the second exponential phase time constant

% Fitting from exponential onset
g = fittype( @(A1, TD1, tau1, A2, TD2, tau2, yRest, x) yRest+A1.*(1-exp(-(x-TD1)/tau1))+A2.*(1-exp(-(x-TD2)/tau2)), ‘problem’, ‘yRest’);
f1 = fit( x(tRest:size(x,1)), y(tRest:size(x,1)), g, ‘problem’, yRest, ‘StartPoint’, [A1start, TD1start, tau1Start, A2start, TD2start, tau2Start], ‘lower’ , [ A1start-100, 0, 5, A2start-200, 10, 10], ‘upper’, [ A1start+200, 20, 40, max(y), 60, 40], ‘MaxIter’, 10000);
pg =coeffvalues(f1);
A1 = pg(1);
TD1 = pg(2);
tau1 = pg(3);
A2 = pg(4);
TD2 = pg(5);
tau2 = pg(6);

yFit( 1:(tRest+floor(TD1)) ) = yRest;
yFit( tRest+floor(TD1)+1 : tRest+floor(TD2) ) = yRest + A1.*(1-exp(-( x(tRest+floor(TD1)+1 : tRest+floor(TD2))-TD1 )/tau1));
yFit( tRest+floor(TD2)+1 : size(y,1)) = yFit(tRest+floor(TD2))+ A2.*(1-exp(-( x(tRest+floor(TD2)+1 : size(y,1))-TD2 )/tau2));

% fitting using Exclude option
f2 = fit( x, y, g, ‘problem’, yRest, ‘StartPoint’, [A1start, TD1start, tau1Start, A2start, TD2start, tau2Start], ‘lower’ , [ 390, 0, 5, A2start-200, 10, 10], ‘upper’, [ 400, 20, 40, max(y), 60, 40], ‘Exclude’, x<0); %Same fitting algortihm that exclude negative x values
pg2=coeffvalues(f2);

A1e = pg2(1);
TD1e = pg2(2);
tau1e = pg2(3);
A2e = pg2(4);
TD2e = pg2(5);
tau2e = pg2(6);

yFite( 1:(tRest+floor(TD1e)) ) = yRest;
yFite( tRest+floor(TD1e)+1 : tRest+floor(TD2e) ) = yRest + A1e.*(1-exp(-( x(tRest+floor(TD1e)+1 : tRest+floor(TD2e))-TD1e )/tau1e));
yFite( tRest+floor(TD2e)+1 : size(y,1)) = yFit(tRest+floor(TD2e))+ A2e.*(1-exp(-( x(tRest+floor(TD2e)+1 : size(y,1))-TD2e )/tau2e));

% Plot the fitting curves
hold on
plot (x, yFit, ‘r’);
plot (x, yFite, ‘k’);Hi everybody!
I’m trying to solve a fitting curve problem concerning a double-exponential fit.
I have this data set (Data.txt) that behaves like a bi-exponential function following a resting condition. Therefore, since x<0 the signal is almost steady whereas, for x>0 the signal presents a first exponential phase (lasting about 20 sec) followed by a second exponential phase.
The problem is that the resulting fitting does not model the signal behavior in the correct way. Specifically, the first exp phase is too short and/or too low even though correct StartingPoint and Upper and Lower limits are set.
Lastly, I’ve written two algorithms with and without "Exclude" options. The second one presents a strange pattern at the onset of the second exp function.
I’ll appreciate any suggestions.

This is the code.

[x, y] = readvars(‘prova.txt’);

tRest = abs(min(x)); % Resting phase duration (sec)
cdp = 20; % estimated duration (sec) of CardioDynamicPhase (cdp)
cdpEpoch = 5; % epoch duration (sec) to compute mean value of amplitude of cdp
plot(x,y);

% Fitting StartingPoint
yRest = mean( y(1:tRest-1) ); % y value at resting condition before exp onset

A1start = mean( y(1:tRest+cdp-cdpEpoch):y(tRest+cdp) )-yRest; % mean y value of the last 5sec of the cardiodynamic phase
TD1start = 0; % starting value (sec) of the first exponential phase onset
tau1Start = 8; % starting value (sec) of the first exponential phase time constant

A2start = max(y)-A1start-yRest; % starting value of the second exponential phase amplitude
TD2start = 30; % starting value (sec) of the second exponential phase onset
tau2Start = 30; % starting value (sec) of the second exponential phase time constant

% Fitting from exponential onset
g = fittype( @(A1, TD1, tau1, A2, TD2, tau2, yRest, x) yRest+A1.*(1-exp(-(x-TD1)/tau1))+A2.*(1-exp(-(x-TD2)/tau2)), ‘problem’, ‘yRest’);
f1 = fit( x(tRest:size(x,1)), y(tRest:size(x,1)), g, ‘problem’, yRest, ‘StartPoint’, [A1start, TD1start, tau1Start, A2start, TD2start, tau2Start], ‘lower’ , [ A1start-100, 0, 5, A2start-200, 10, 10], ‘upper’, [ A1start+200, 20, 40, max(y), 60, 40], ‘MaxIter’, 10000);
pg =coeffvalues(f1);
A1 = pg(1);
TD1 = pg(2);
tau1 = pg(3);
A2 = pg(4);
TD2 = pg(5);
tau2 = pg(6);

yFit( 1:(tRest+floor(TD1)) ) = yRest;
yFit( tRest+floor(TD1)+1 : tRest+floor(TD2) ) = yRest + A1.*(1-exp(-( x(tRest+floor(TD1)+1 : tRest+floor(TD2))-TD1 )/tau1));
yFit( tRest+floor(TD2)+1 : size(y,1)) = yFit(tRest+floor(TD2))+ A2.*(1-exp(-( x(tRest+floor(TD2)+1 : size(y,1))-TD2 )/tau2));

% fitting using Exclude option
f2 = fit( x, y, g, ‘problem’, yRest, ‘StartPoint’, [A1start, TD1start, tau1Start, A2start, TD2start, tau2Start], ‘lower’ , [ 390, 0, 5, A2start-200, 10, 10], ‘upper’, [ 400, 20, 40, max(y), 60, 40], ‘Exclude’, x<0); %Same fitting algortihm that exclude negative x values
pg2=coeffvalues(f2);

A1e = pg2(1);
TD1e = pg2(2);
tau1e = pg2(3);
A2e = pg2(4);
TD2e = pg2(5);
tau2e = pg2(6);

yFite( 1:(tRest+floor(TD1e)) ) = yRest;
yFite( tRest+floor(TD1e)+1 : tRest+floor(TD2e) ) = yRest + A1e.*(1-exp(-( x(tRest+floor(TD1e)+1 : tRest+floor(TD2e))-TD1e )/tau1e));
yFite( tRest+floor(TD2e)+1 : size(y,1)) = yFit(tRest+floor(TD2e))+ A2e.*(1-exp(-( x(tRest+floor(TD2e)+1 : size(y,1))-TD2e )/tau2e));

% Plot the fitting curves
hold on
plot (x, yFit, ‘r’);
plot (x, yFite, ‘k’); Hi everybody!
I’m trying to solve a fitting curve problem concerning a double-exponential fit.
I have this data set (Data.txt) that behaves like a bi-exponential function following a resting condition. Therefore, since x<0 the signal is almost steady whereas, for x>0 the signal presents a first exponential phase (lasting about 20 sec) followed by a second exponential phase.
The problem is that the resulting fitting does not model the signal behavior in the correct way. Specifically, the first exp phase is too short and/or too low even though correct StartingPoint and Upper and Lower limits are set.
Lastly, I’ve written two algorithms with and without "Exclude" options. The second one presents a strange pattern at the onset of the second exp function.
I’ll appreciate any suggestions.

This is the code.

[x, y] = readvars(‘prova.txt’);

tRest = abs(min(x)); % Resting phase duration (sec)
cdp = 20; % estimated duration (sec) of CardioDynamicPhase (cdp)
cdpEpoch = 5; % epoch duration (sec) to compute mean value of amplitude of cdp
plot(x,y);

% Fitting StartingPoint
yRest = mean( y(1:tRest-1) ); % y value at resting condition before exp onset

A1start = mean( y(1:tRest+cdp-cdpEpoch):y(tRest+cdp) )-yRest; % mean y value of the last 5sec of the cardiodynamic phase
TD1start = 0; % starting value (sec) of the first exponential phase onset
tau1Start = 8; % starting value (sec) of the first exponential phase time constant

A2start = max(y)-A1start-yRest; % starting value of the second exponential phase amplitude
TD2start = 30; % starting value (sec) of the second exponential phase onset
tau2Start = 30; % starting value (sec) of the second exponential phase time constant

% Fitting from exponential onset
g = fittype( @(A1, TD1, tau1, A2, TD2, tau2, yRest, x) yRest+A1.*(1-exp(-(x-TD1)/tau1))+A2.*(1-exp(-(x-TD2)/tau2)), ‘problem’, ‘yRest’);
f1 = fit( x(tRest:size(x,1)), y(tRest:size(x,1)), g, ‘problem’, yRest, ‘StartPoint’, [A1start, TD1start, tau1Start, A2start, TD2start, tau2Start], ‘lower’ , [ A1start-100, 0, 5, A2start-200, 10, 10], ‘upper’, [ A1start+200, 20, 40, max(y), 60, 40], ‘MaxIter’, 10000);
pg =coeffvalues(f1);
A1 = pg(1);
TD1 = pg(2);
tau1 = pg(3);
A2 = pg(4);
TD2 = pg(5);
tau2 = pg(6);

yFit( 1:(tRest+floor(TD1)) ) = yRest;
yFit( tRest+floor(TD1)+1 : tRest+floor(TD2) ) = yRest + A1.*(1-exp(-( x(tRest+floor(TD1)+1 : tRest+floor(TD2))-TD1 )/tau1));
yFit( tRest+floor(TD2)+1 : size(y,1)) = yFit(tRest+floor(TD2))+ A2.*(1-exp(-( x(tRest+floor(TD2)+1 : size(y,1))-TD2 )/tau2));

% fitting using Exclude option
f2 = fit( x, y, g, ‘problem’, yRest, ‘StartPoint’, [A1start, TD1start, tau1Start, A2start, TD2start, tau2Start], ‘lower’ , [ 390, 0, 5, A2start-200, 10, 10], ‘upper’, [ 400, 20, 40, max(y), 60, 40], ‘Exclude’, x<0); %Same fitting algortihm that exclude negative x values
pg2=coeffvalues(f2);

A1e = pg2(1);
TD1e = pg2(2);
tau1e = pg2(3);
A2e = pg2(4);
TD2e = pg2(5);
tau2e = pg2(6);

yFite( 1:(tRest+floor(TD1e)) ) = yRest;
yFite( tRest+floor(TD1e)+1 : tRest+floor(TD2e) ) = yRest + A1e.*(1-exp(-( x(tRest+floor(TD1e)+1 : tRest+floor(TD2e))-TD1e )/tau1e));
yFite( tRest+floor(TD2e)+1 : size(y,1)) = yFit(tRest+floor(TD2e))+ A2e.*(1-exp(-( x(tRest+floor(TD2e)+1 : size(y,1))-TD2e )/tau2e));

% Plot the fitting curves
hold on
plot (x, yFit, ‘r’);
plot (x, yFite, ‘k’); signal fitting MATLAB Answers — New Questions

​

Error in port widths or dimensions. Output port 1 of ‘last/Subsystem/powergui/EquivalentModel1/Gates/From2’ is a one dimensional vector with 2 elements.

AND

Error in port widths or dimensions. Invalid dimension has been specified for input port 2 of ‘last/Subsystem/powergui/EquivalentModel1/Gates/Mux’.

I am using MATLAB 2010a. problem occurred in the switching circuit of invert.
Anticipating your answers…

<</matlabcentral/answers/uploaded_files/38534/1.png>>

<</matlabcentral/answers/uploaded_files/38535/2.png>>Error in port widths or dimensions. Output port 1 of ‘last/Subsystem/powergui/EquivalentModel1/Gates/From2’ is a one dimensional vector with 2 elements.

AND

Error in port widths or dimensions. Invalid dimension has been specified for input port 2 of ‘last/Subsystem/powergui/EquivalentModel1/Gates/Mux’.

I am using MATLAB 2010a. problem occurred in the switching circuit of invert.
Anticipating your answers…

<</matlabcentral/answers/uploaded_files/38534/1.png>>

<</matlabcentral/answers/uploaded_files/38535/2.png>> Error in port widths or dimensions. Output port 1 of ‘last/Subsystem/powergui/EquivalentModel1/Gates/From2’ is a one dimensional vector with 2 elements.

AND

Error in port widths or dimensions. Invalid dimension has been specified for input port 2 of ‘last/Subsystem/powergui/EquivalentModel1/Gates/Mux’.

I am using MATLAB 2010a. problem occurred in the switching circuit of invert.
Anticipating your answers…

<</matlabcentral/answers/uploaded_files/38534/1.png>>

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