how can I link subplots (for brush data)?
this is my code :
colors = {‘r’, ‘g’, ‘b’, ‘c’, ‘m’, ‘y’, ‘k’, [0, 0.5, 0.5], [0.5, 0.5, 0], [0, 0.5, 0], [0.5, 0, 0],[0, 0, 0.5], [0.5, 0.5, 0.5],[0.3010 0.7450 0.9330],[0.4660 0.6740 0.1880],[0.4940 0.1840 0.5560],[0.9290 0.6940 0.1250],[0.8500 0.3250 0.0980],[0.6350, 0.0780, 0.1840],[0 0.4470 0.7410] };

[filename,pathname] = uigetfile(‘*.csv’, ‘Select CSV files’, ‘MultiSelect’, ‘on’);

if isnumeric(filename) % user clicked Cancel
return
end

s=size(filename,2);

for i=1:s

o= readtable(fullfile(pathname,filename{i}));

cData1=table2array(o(:,1));
cData2=table2array(o(:,2));

fig = figure();
subplot(3,1,1)
p1=plot( cData1,cData2,…
‘Color’, colors{mod(i, numel(colors)) + 1});

hold(‘on’)
end

for i=1:s

o= readtable(fullfile(pathname,filename{i}));

cData1=table2array(o(:,1));
cData3=table2array(o(:,3));
subplot(3,1,2)
p2=plot( cData1,cData3,…
‘Color’, colors{mod(i, numel(colors)) + 1});

% grid(app.UIAxes2_2,’on’)
hold(‘on’)
end

%%%%%%%%%%%%%%%%%%%%%%%%

for i=1:s

o= readtable(fullfile(pathname,filename{i}));

cData1=table2array(o(:,1));
cData4=table2array(o(:,4));
subplot(3,1,3)
p3=plot( cData1,cData4,…
‘Color’, colors{mod(i, numel(colors)) + 1});

% grid(app.UIAxes2,’on’)
hold(‘on’)
end

b=brush(fig);
b.ActionPostCallback = {@onBrushAction,p1,p2,p3};

% callback function
function onBrushAction(~, eventdata,p1,p2,p3)

set(p1, ‘BrushData’, eventdata.Axes.Children.BrushData)
set(p2, ‘BrushData’, eventdata.Axes.Children.BrushData)
set(p3, ‘BrushData’, eventdata.Axes.Children.BrushData)

endhow can I link subplots (for brush data)?
this is my code :
colors = {‘r’, ‘g’, ‘b’, ‘c’, ‘m’, ‘y’, ‘k’, [0, 0.5, 0.5], [0.5, 0.5, 0], [0, 0.5, 0], [0.5, 0, 0],[0, 0, 0.5], [0.5, 0.5, 0.5],[0.3010 0.7450 0.9330],[0.4660 0.6740 0.1880],[0.4940 0.1840 0.5560],[0.9290 0.6940 0.1250],[0.8500 0.3250 0.0980],[0.6350, 0.0780, 0.1840],[0 0.4470 0.7410] };

[filename,pathname] = uigetfile(‘*.csv’, ‘Select CSV files’, ‘MultiSelect’, ‘on’);

if isnumeric(filename) % user clicked Cancel
return
end

s=size(filename,2);

for i=1:s

o= readtable(fullfile(pathname,filename{i}));

cData1=table2array(o(:,1));
cData2=table2array(o(:,2));

fig = figure();
subplot(3,1,1)
p1=plot( cData1,cData2,…
‘Color’, colors{mod(i, numel(colors)) + 1});

hold(‘on’)
end

for i=1:s

o= readtable(fullfile(pathname,filename{i}));

cData1=table2array(o(:,1));
cData3=table2array(o(:,3));
subplot(3,1,2)
p2=plot( cData1,cData3,…
‘Color’, colors{mod(i, numel(colors)) + 1});

% grid(app.UIAxes2_2,’on’)
hold(‘on’)
end

%%%%%%%%%%%%%%%%%%%%%%%%

for i=1:s

o= readtable(fullfile(pathname,filename{i}));

cData1=table2array(o(:,1));
cData4=table2array(o(:,4));
subplot(3,1,3)
p3=plot( cData1,cData4,…
‘Color’, colors{mod(i, numel(colors)) + 1});

% grid(app.UIAxes2,’on’)
hold(‘on’)
end

b=brush(fig);
b.ActionPostCallback = {@onBrushAction,p1,p2,p3};

% callback function
function onBrushAction(~, eventdata,p1,p2,p3)

set(p1, ‘BrushData’, eventdata.Axes.Children.BrushData)
set(p2, ‘BrushData’, eventdata.Axes.Children.BrushData)
set(p3, ‘BrushData’, eventdata.Axes.Children.BrushData)

end how can I link subplots (for brush data)?
this is my code :
colors = {‘r’, ‘g’, ‘b’, ‘c’, ‘m’, ‘y’, ‘k’, [0, 0.5, 0.5], [0.5, 0.5, 0], [0, 0.5, 0], [0.5, 0, 0],[0, 0, 0.5], [0.5, 0.5, 0.5],[0.3010 0.7450 0.9330],[0.4660 0.6740 0.1880],[0.4940 0.1840 0.5560],[0.9290 0.6940 0.1250],[0.8500 0.3250 0.0980],[0.6350, 0.0780, 0.1840],[0 0.4470 0.7410] };

[filename,pathname] = uigetfile(‘*.csv’, ‘Select CSV files’, ‘MultiSelect’, ‘on’);

if isnumeric(filename) % user clicked Cancel
return
end

s=size(filename,2);

for i=1:s

o= readtable(fullfile(pathname,filename{i}));

cData1=table2array(o(:,1));
cData2=table2array(o(:,2));

fig = figure();
subplot(3,1,1)
p1=plot( cData1,cData2,…
‘Color’, colors{mod(i, numel(colors)) + 1});

hold(‘on’)
end

for i=1:s

o= readtable(fullfile(pathname,filename{i}));

cData1=table2array(o(:,1));
cData3=table2array(o(:,3));
subplot(3,1,2)
p2=plot( cData1,cData3,…
‘Color’, colors{mod(i, numel(colors)) + 1});

% grid(app.UIAxes2_2,’on’)
hold(‘on’)
end

%%%%%%%%%%%%%%%%%%%%%%%%

for i=1:s

o= readtable(fullfile(pathname,filename{i}));

cData1=table2array(o(:,1));
cData4=table2array(o(:,4));
subplot(3,1,3)
p3=plot( cData1,cData4,…
‘Color’, colors{mod(i, numel(colors)) + 1});

% grid(app.UIAxes2,’on’)
hold(‘on’)
end

b=brush(fig);
b.ActionPostCallback = {@onBrushAction,p1,p2,p3};

% callback function
function onBrushAction(~, eventdata,p1,p2,p3)

set(p1, ‘BrushData’, eventdata.Axes.Children.BrushData)
set(p2, ‘BrushData’, eventdata.Axes.Children.BrushData)
set(p3, ‘BrushData’, eventdata.Axes.Children.BrushData)

end plot, brush, link, data MATLAB Answers — New Questions

​

I need to generate IPs for HDL implementation of an algorithm. I need to do fftshift in simulink but there is no fftshift block avaiable. Is there any way we can do fftshift of the output data from FFT block from DSP HDL toolbox? I am attaching FFT model for reference.I need to generate IPs for HDL implementation of an algorithm. I need to do fftshift in simulink but there is no fftshift block avaiable. Is there any way we can do fftshift of the output data from FFT block from DSP HDL toolbox? I am attaching FFT model for reference. I need to generate IPs for HDL implementation of an algorithm. I need to do fftshift in simulink but there is no fftshift block avaiable. Is there any way we can do fftshift of the output data from FFT block from DSP HDL toolbox? I am attaching FFT model for reference. fft, fftshift, hdl MATLAB Answers — New Questions

​

Hi,

I need to use simulink to solve the algebraic Riccati equation. How can I do?

I ‘m using the LQR for vibrations control.Hi,

I need to use simulink to solve the algebraic Riccati equation. How can I do?

I ‘m using the LQR for vibrations control. Hi,

I need to use simulink to solve the algebraic Riccati equation. How can I do?

I ‘m using the LQR for vibrations control. riccati, simulink MATLAB Answers — New Questions

​

In an optimisation problem I have 4 parameters to optimise. Once the algorithm converges, why does it assign the upper bound value to the first parameter, while assigning the lower bounds to the second and third?In an optimisation problem I have 4 parameters to optimise. Once the algorithm converges, why does it assign the upper bound value to the first parameter, while assigning the lower bounds to the second and third? In an optimisation problem I have 4 parameters to optimise. Once the algorithm converges, why does it assign the upper bound value to the first parameter, while assigning the lower bounds to the second and third? least square regression, lsqnonlin, curve fitting, optimization MATLAB Answers — New Questions

​

I am trying to add two columns of data together to create a new column, but I am receiving an error message and the data is not being added appropriately.
For example, I want to create something where I add the values of R and L to create Z:
R L Z
1 7 8
2 2 4
3 5 8
4 1 5

However when using a simple code like:
Z = R + L
We are receiving an error. I tried using the sum function, but it is simply adding all the vairables together, but not adding each row individually to create this variable Z that is a new column.I am trying to add two columns of data together to create a new column, but I am receiving an error message and the data is not being added appropriately.
For example, I want to create something where I add the values of R and L to create Z:
R L Z
1 7 8
2 2 4
3 5 8
4 1 5

However when using a simple code like:
Z = R + L
We are receiving an error. I tried using the sum function, but it is simply adding all the vairables together, but not adding each row individually to create this variable Z that is a new column. I am trying to add two columns of data together to create a new column, but I am receiving an error message and the data is not being added appropriately.
For example, I want to create something where I add the values of R and L to create Z:
R L Z
1 7 8
2 2 4
3 5 8
4 1 5

However when using a simple code like:
Z = R + L
We are receiving an error. I tried using the sum function, but it is simply adding all the vairables together, but not adding each row individually to create this variable Z that is a new column. columns, matrix array MATLAB Answers — New Questions

​

Hi,
I am attempting to compute the Frequency Response Function (FRF) of a linear system using Welch’s method for estimating the Power Spectral Density (PSD). However, the results I obtain lack the sharp peaks expected for a linear system, and I would like guidance on whether my code or approach is correct, or if I might be missing something fundamental.
Despite using these settings and varying the parameters, the FRF I compute does not show sharp peaks that I expect for a linear system. The peaks are relatively broad, which makes it difficult to identify resonant frequencies clearly. I am uncertain whether:
My choice of parameters (e.g., nfft,window_length,noverlapnfft, window_length, noverlapnfft,window_length,noverlap) is contributing to this issue.
There might be a problem in my preprocessing or interpretation of the data.
Please advise, thankyou!
Fs = 8192;
window_length = 3144;
noverlap = 0.75*window_length;
nfft = 2048;

FileName = ‘exp1_0’;
FileStart = 1;
FileEnd = 10;
fileDirectory = ‘D:xx’;

% Define the window function
window_function = hanning(window_length);

% Initialize accumulators for averaging
sum_Pxx = 0; % Sum of auto-power spectrum of voltage
sum_Pxy = 0; % Sum of cross-power spectrum

% Loop over all test files = averaging
for filenum = FileStart:FileEnd

% Load data from each file
fullFileName = [fileDirectory, FileName, sprintf(‘%02d’, filenum), ‘.mat’];
fileData = load(fullFileName);

% Extract Velocity and Voltage Data
velocity_data = fileData.([FileName, sprintf(‘%02d’, filenum)]).Y(4).Data; % Output: Velocity (m/s)
voltage_data = fileData.([FileName, sprintf(‘%02d’, filenum)]).Y(3).Data; % Input: Voltage (V)

% Compute PSDs using Welch’s method
[Pxx, f] = pwelch(voltage_data, window_function, noverlap, nfft, Fs); % Auto-power spectrum of voltage
[Pxy, ~] = cpsd(velocity_data, voltage_data, window_function, noverlap, nfft, Fs); % Cross-power spectrum

% Accumulate results for averaging
sum_Pxx = sum_Pxx + Pxx;
sum_Pxy = sum_Pxy + Pxy;
end

% Compute FRF (H = Pxy / Pxx)
H = sum_Pxy ./ sum_Pxx;

% Convert to dB
H_mag_dB = 20 * log10(abs(H));

% Apply smoothing and moving average
window_size = 5;
smoothed_H_w = smoothdata(H_mag_dB, ‘movmean’, window_size);

% Plot FRF
figure;
plot(f, smoothed_H_w , ‘r’);
hold on;
title([‘FRF of Random Test with – Window Length: ‘, num2str(window_length), …
‘, Overlap: ‘, num2str(noverlap), …
‘, nfft: ‘, num2str(nfft)]);
xlabel(‘Frequency (Hz)’);
ylabel(‘Magnitude (dB)’);
grid on;
xlim([1 300]);
hold off;Hi,
I am attempting to compute the Frequency Response Function (FRF) of a linear system using Welch’s method for estimating the Power Spectral Density (PSD). However, the results I obtain lack the sharp peaks expected for a linear system, and I would like guidance on whether my code or approach is correct, or if I might be missing something fundamental.
Despite using these settings and varying the parameters, the FRF I compute does not show sharp peaks that I expect for a linear system. The peaks are relatively broad, which makes it difficult to identify resonant frequencies clearly. I am uncertain whether:
My choice of parameters (e.g., nfft,window_length,noverlapnfft, window_length, noverlapnfft,window_length,noverlap) is contributing to this issue.
There might be a problem in my preprocessing or interpretation of the data.
Please advise, thankyou!
Fs = 8192;
window_length = 3144;
noverlap = 0.75*window_length;
nfft = 2048;

FileName = ‘exp1_0’;
FileStart = 1;
FileEnd = 10;
fileDirectory = ‘D:xx’;

% Define the window function
window_function = hanning(window_length);

% Initialize accumulators for averaging
sum_Pxx = 0; % Sum of auto-power spectrum of voltage
sum_Pxy = 0; % Sum of cross-power spectrum

% Loop over all test files = averaging
for filenum = FileStart:FileEnd

% Load data from each file
fullFileName = [fileDirectory, FileName, sprintf(‘%02d’, filenum), ‘.mat’];
fileData = load(fullFileName);

% Extract Velocity and Voltage Data
velocity_data = fileData.([FileName, sprintf(‘%02d’, filenum)]).Y(4).Data; % Output: Velocity (m/s)
voltage_data = fileData.([FileName, sprintf(‘%02d’, filenum)]).Y(3).Data; % Input: Voltage (V)

% Compute PSDs using Welch’s method
[Pxx, f] = pwelch(voltage_data, window_function, noverlap, nfft, Fs); % Auto-power spectrum of voltage
[Pxy, ~] = cpsd(velocity_data, voltage_data, window_function, noverlap, nfft, Fs); % Cross-power spectrum

% Accumulate results for averaging
sum_Pxx = sum_Pxx + Pxx;
sum_Pxy = sum_Pxy + Pxy;
end

% Compute FRF (H = Pxy / Pxx)
H = sum_Pxy ./ sum_Pxx;

% Convert to dB
H_mag_dB = 20 * log10(abs(H));

% Apply smoothing and moving average
window_size = 5;
smoothed_H_w = smoothdata(H_mag_dB, ‘movmean’, window_size);

% Plot FRF
figure;
plot(f, smoothed_H_w , ‘r’);
hold on;
title([‘FRF of Random Test with – Window Length: ‘, num2str(window_length), …
‘, Overlap: ‘, num2str(noverlap), …
‘, nfft: ‘, num2str(nfft)]);
xlabel(‘Frequency (Hz)’);
ylabel(‘Magnitude (dB)’);
grid on;
xlim([1 300]);
hold off; Hi,
I am attempting to compute the Frequency Response Function (FRF) of a linear system using Welch’s method for estimating the Power Spectral Density (PSD). However, the results I obtain lack the sharp peaks expected for a linear system, and I would like guidance on whether my code or approach is correct, or if I might be missing something fundamental.
Despite using these settings and varying the parameters, the FRF I compute does not show sharp peaks that I expect for a linear system. The peaks are relatively broad, which makes it difficult to identify resonant frequencies clearly. I am uncertain whether:
My choice of parameters (e.g., nfft,window_length,noverlapnfft, window_length, noverlapnfft,window_length,noverlap) is contributing to this issue.
There might be a problem in my preprocessing or interpretation of the data.
Please advise, thankyou!
Fs = 8192;
window_length = 3144;
noverlap = 0.75*window_length;
nfft = 2048;

FileName = ‘exp1_0’;
FileStart = 1;
FileEnd = 10;
fileDirectory = ‘D:xx’;

% Define the window function
window_function = hanning(window_length);

% Initialize accumulators for averaging
sum_Pxx = 0; % Sum of auto-power spectrum of voltage
sum_Pxy = 0; % Sum of cross-power spectrum

% Loop over all test files = averaging
for filenum = FileStart:FileEnd

% Load data from each file
fullFileName = [fileDirectory, FileName, sprintf(‘%02d’, filenum), ‘.mat’];
fileData = load(fullFileName);

% Extract Velocity and Voltage Data
velocity_data = fileData.([FileName, sprintf(‘%02d’, filenum)]).Y(4).Data; % Output: Velocity (m/s)
voltage_data = fileData.([FileName, sprintf(‘%02d’, filenum)]).Y(3).Data; % Input: Voltage (V)

% Compute PSDs using Welch’s method
[Pxx, f] = pwelch(voltage_data, window_function, noverlap, nfft, Fs); % Auto-power spectrum of voltage
[Pxy, ~] = cpsd(velocity_data, voltage_data, window_function, noverlap, nfft, Fs); % Cross-power spectrum

% Accumulate results for averaging
sum_Pxx = sum_Pxx + Pxx;
sum_Pxy = sum_Pxy + Pxy;
end

% Compute FRF (H = Pxy / Pxx)
H = sum_Pxy ./ sum_Pxx;

% Convert to dB
H_mag_dB = 20 * log10(abs(H));

% Apply smoothing and moving average
window_size = 5;
smoothed_H_w = smoothdata(H_mag_dB, ‘movmean’, window_size);

% Plot FRF
figure;
plot(f, smoothed_H_w , ‘r’);
hold on;
title([‘FRF of Random Test with – Window Length: ‘, num2str(window_length), …
‘, Overlap: ‘, num2str(noverlap), …
‘, nfft: ‘, num2str(nfft)]);
xlabel(‘Frequency (Hz)’);
ylabel(‘Magnitude (dB)’);
grid on;
xlim([1 300]);
hold off; fft, psd MATLAB Answers — New Questions

​

Hello,
I am trying to construct and train a neural network to do a regression task for multi-output problem. However, I am starting with a toy problem with single output in order to understand how things work. I tried to use both functions ‘trainNetwork’ and ‘trainnet’ but both do not work as expected. In the toy problem, I am trying to approximate a simple function using the neural network with only 2 layers of 20 neuros per layer. The network does not converge for either of the functions above.
Could you tell me if I am doing something wrong in the way I am using them. Thank you

clear; clc; close all;
t = linspace(0,2*pi,500);
s = cos(2*pi*t).*exp(0.1*t) + sech(0.2*t);
%%
% Normalize inputs to the range [0, 1]
input_min = min(t); % Overall minimum
input_max = max(t); % Overall maximum
normalized_inputs = (t – input_min) / (input_max – input_min);
% Normalize outputs to the range [0, 1]
output_min = min(s); % Overall minimum
output_max = max(s); % Overall maximum
normalized_outputs = (s – output_min) / (output_max – output_min);
%%
% Define the architecture
layers = [
featureInputLayer(size(normalized_inputs, 1)) % Input layer
fullyConnectedLayer(20) % Hidden layer with 20 neurons
reluLayer % Activation function
fullyConnectedLayer(20) % Hidden layer with 20 neurons
reluLayer % Activation function
fullyConnectedLayer(size(normalized_outputs, 1)) % Output layer
regressionLayer % Regression layer for continuous outputs
];
idx = randperm(numel(t));
train_idx = idx(1:round(0.8*numel(t)));
val_idx = idx(round(0.8*numel(t))+1:end);
t_train = normalized_inputs(train_idx);
s_train = normalized_outputs(train_idx);
t_val = normalized_inputs(val_idx);
s_val = normalized_outputs(val_idx);
miniBatchSize = 32;
options = trainingOptions(‘adam’, …
‘MiniBatchSize’, miniBatchSize, …
‘MaxEpochs’, 2000, …
‘InitialLearnRate’, 0.001, …
‘LearnRateSchedule’, ‘piecewise’, …
‘LearnRateDropFactor’, 0.1, …
‘LearnRateDropPeriod’, 1000, …
‘Shuffle’, ‘every-epoch’, …
‘ValidationData’, {t_val’, s_val’}, …
‘ValidationFrequency’, 10, …
‘Plots’, ‘training-progress’, …
‘Verbose’, true);
% Train the network
% net = trainnet(normalized_inputs’, normalized_outputs’, layers, "mse", options);
net = trainNetwork(normalized_inputs’, normalized_outputs’, layers, options);
% net = fitrnet(normalized_inputs’, normalized_outputs’, ‘Activations’, "tanh" , …
% "LayerSizes",[50 50 50]);
% Predict using the trained network
normalized_predictions = predict(net, normalized_inputs’)’;
% De-standardize predictions
predictions = normalized_predictions .* (output_max – output_min) + output_min;
% Evaluate performance (e.g., Mean Squared Error)
mse_loss = mean((normalized_outputs – normalized_predictions).^2, ‘all’);
fprintf(‘MSE: %.4fn’, mse_loss);
%%
figure(‘units’,’normalized’,’outerposition’,[0 0 1 1])
plot(t,normalized_outputs,’r’);
grid;
hold on
plot(t,normalized_predictions,’–b’);
legend(‘Truth’,’NN’,’Location’,’best’);
set(gcf,’color’,’w’)Hello,
I am trying to construct and train a neural network to do a regression task for multi-output problem. However, I am starting with a toy problem with single output in order to understand how things work. I tried to use both functions ‘trainNetwork’ and ‘trainnet’ but both do not work as expected. In the toy problem, I am trying to approximate a simple function using the neural network with only 2 layers of 20 neuros per layer. The network does not converge for either of the functions above.
Could you tell me if I am doing something wrong in the way I am using them. Thank you

clear; clc; close all;
t = linspace(0,2*pi,500);
s = cos(2*pi*t).*exp(0.1*t) + sech(0.2*t);
%%
% Normalize inputs to the range [0, 1]
input_min = min(t); % Overall minimum
input_max = max(t); % Overall maximum
normalized_inputs = (t – input_min) / (input_max – input_min);
% Normalize outputs to the range [0, 1]
output_min = min(s); % Overall minimum
output_max = max(s); % Overall maximum
normalized_outputs = (s – output_min) / (output_max – output_min);
%%
% Define the architecture
layers = [
featureInputLayer(size(normalized_inputs, 1)) % Input layer
fullyConnectedLayer(20) % Hidden layer with 20 neurons
reluLayer % Activation function
fullyConnectedLayer(20) % Hidden layer with 20 neurons
reluLayer % Activation function
fullyConnectedLayer(size(normalized_outputs, 1)) % Output layer
regressionLayer % Regression layer for continuous outputs
];
idx = randperm(numel(t));
train_idx = idx(1:round(0.8*numel(t)));
val_idx = idx(round(0.8*numel(t))+1:end);
t_train = normalized_inputs(train_idx);
s_train = normalized_outputs(train_idx);
t_val = normalized_inputs(val_idx);
s_val = normalized_outputs(val_idx);
miniBatchSize = 32;
options = trainingOptions(‘adam’, …
‘MiniBatchSize’, miniBatchSize, …
‘MaxEpochs’, 2000, …
‘InitialLearnRate’, 0.001, …
‘LearnRateSchedule’, ‘piecewise’, …
‘LearnRateDropFactor’, 0.1, …
‘LearnRateDropPeriod’, 1000, …
‘Shuffle’, ‘every-epoch’, …
‘ValidationData’, {t_val’, s_val’}, …
‘ValidationFrequency’, 10, …
‘Plots’, ‘training-progress’, …
‘Verbose’, true);
% Train the network
% net = trainnet(normalized_inputs’, normalized_outputs’, layers, "mse", options);
net = trainNetwork(normalized_inputs’, normalized_outputs’, layers, options);
% net = fitrnet(normalized_inputs’, normalized_outputs’, ‘Activations’, "tanh" , …
% "LayerSizes",[50 50 50]);
% Predict using the trained network
normalized_predictions = predict(net, normalized_inputs’)’;
% De-standardize predictions
predictions = normalized_predictions .* (output_max – output_min) + output_min;
% Evaluate performance (e.g., Mean Squared Error)
mse_loss = mean((normalized_outputs – normalized_predictions).^2, ‘all’);
fprintf(‘MSE: %.4fn’, mse_loss);
%%
figure(‘units’,’normalized’,’outerposition’,[0 0 1 1])
plot(t,normalized_outputs,’r’);
grid;
hold on
plot(t,normalized_predictions,’–b’);
legend(‘Truth’,’NN’,’Location’,’best’);
set(gcf,’color’,’w’) Hello,
I am trying to construct and train a neural network to do a regression task for multi-output problem. However, I am starting with a toy problem with single output in order to understand how things work. I tried to use both functions ‘trainNetwork’ and ‘trainnet’ but both do not work as expected. In the toy problem, I am trying to approximate a simple function using the neural network with only 2 layers of 20 neuros per layer. The network does not converge for either of the functions above.
Could you tell me if I am doing something wrong in the way I am using them. Thank you

clear; clc; close all;
t = linspace(0,2*pi,500);
s = cos(2*pi*t).*exp(0.1*t) + sech(0.2*t);
%%
% Normalize inputs to the range [0, 1]
input_min = min(t); % Overall minimum
input_max = max(t); % Overall maximum
normalized_inputs = (t – input_min) / (input_max – input_min);
% Normalize outputs to the range [0, 1]
output_min = min(s); % Overall minimum
output_max = max(s); % Overall maximum
normalized_outputs = (s – output_min) / (output_max – output_min);
%%
% Define the architecture
layers = [
featureInputLayer(size(normalized_inputs, 1)) % Input layer
fullyConnectedLayer(20) % Hidden layer with 20 neurons
reluLayer % Activation function
fullyConnectedLayer(20) % Hidden layer with 20 neurons
reluLayer % Activation function
fullyConnectedLayer(size(normalized_outputs, 1)) % Output layer
regressionLayer % Regression layer for continuous outputs
];
idx = randperm(numel(t));
train_idx = idx(1:round(0.8*numel(t)));
val_idx = idx(round(0.8*numel(t))+1:end);
t_train = normalized_inputs(train_idx);
s_train = normalized_outputs(train_idx);
t_val = normalized_inputs(val_idx);
s_val = normalized_outputs(val_idx);
miniBatchSize = 32;
options = trainingOptions(‘adam’, …
‘MiniBatchSize’, miniBatchSize, …
‘MaxEpochs’, 2000, …
‘InitialLearnRate’, 0.001, …
‘LearnRateSchedule’, ‘piecewise’, …
‘LearnRateDropFactor’, 0.1, …
‘LearnRateDropPeriod’, 1000, …
‘Shuffle’, ‘every-epoch’, …
‘ValidationData’, {t_val’, s_val’}, …
‘ValidationFrequency’, 10, …
‘Plots’, ‘training-progress’, …
‘Verbose’, true);
% Train the network
% net = trainnet(normalized_inputs’, normalized_outputs’, layers, "mse", options);
net = trainNetwork(normalized_inputs’, normalized_outputs’, layers, options);
% net = fitrnet(normalized_inputs’, normalized_outputs’, ‘Activations’, "tanh" , …
% "LayerSizes",[50 50 50]);
% Predict using the trained network
normalized_predictions = predict(net, normalized_inputs’)’;
% De-standardize predictions
predictions = normalized_predictions .* (output_max – output_min) + output_min;
% Evaluate performance (e.g., Mean Squared Error)
mse_loss = mean((normalized_outputs – normalized_predictions).^2, ‘all’);
fprintf(‘MSE: %.4fn’, mse_loss);
%%
figure(‘units’,’normalized’,’outerposition’,[0 0 1 1])
plot(t,normalized_outputs,’r’);
grid;
hold on
plot(t,normalized_predictions,’–b’);
legend(‘Truth’,’NN’,’Location’,’best’);
set(gcf,’color’,’w’) neural network, neural networks, regression MATLAB Answers — New Questions

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Hello,
I’m realatively new to simulink and I am trying to generate a P-V curve using a solar cell module and a variable resistor. Whenever I use the default values for Voc and Isc in the solar cell module, I get a P-V curve. However, when I change the Voc and Isc values in the solar cell, my plot shows a linear relationship instead of a curve. Is there a way to change the Voc and Isc values in the solar cell to have it display a correct P-V curve? I have provided my working Simulink model below. Thank you.Hello,
I’m realatively new to simulink and I am trying to generate a P-V curve using a solar cell module and a variable resistor. Whenever I use the default values for Voc and Isc in the solar cell module, I get a P-V curve. However, when I change the Voc and Isc values in the solar cell, my plot shows a linear relationship instead of a curve. Is there a way to change the Voc and Isc values in the solar cell to have it display a correct P-V curve? I have provided my working Simulink model below. Thank you. Hello,
I’m realatively new to simulink and I am trying to generate a P-V curve using a solar cell module and a variable resistor. Whenever I use the default values for Voc and Isc in the solar cell module, I get a P-V curve. However, when I change the Voc and Isc values in the solar cell, my plot shows a linear relationship instead of a curve. Is there a way to change the Voc and Isc values in the solar cell to have it display a correct P-V curve? I have provided my working Simulink model below. Thank you. solar cell, open circuit voltage, short circuit current MATLAB Answers — New Questions

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I need an algorithm to fill in the blank vertical path pixels.I need an algorithm to fill in the blank vertical path pixels. I need an algorithm to fill in the blank vertical path pixels. complete lines, continuity, edge linking MATLAB Answers — New Questions

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I implemented a constitutive model for the bovine pericardium to be fit with experimental data to derive four model parameters. The code runs, but I have doubts about the correct implementation of the error function. Showing the iterations on the screen, I notice that at certain points the objective function returns nan or inf. I have checked the experimental data and in some cases there are values close to zero, but I don’t think this justifies the fact that the function converges, thus giving me the 4 parameters I am looking for, but without obtaining a decent fitting with the experimental data. Moreover, optimized parameters number 2 and 3 are assigned to lower bounds, while the first optimized parameter is very high. Below is the main and the error function.
Main:
clc; clear all;close all;
load ("data_11122.mat"); % change lot number if necessary
clearvars -except data

%Model requires lambda= x/x0 instead of epsilon: calculate lambda
% from exp data and store in data struct

stress=data(1).stress; %change index to change dogbone if necessary
strain=data(1).strain; %change index to change dogbone if necessary
% stress=stress/max(abs(stress));
% strain=strain/max(abs(strain));
p_initial=[0.5 0.5 0.5 0.5];
a0=[1 0 0];
%vincoli su zeta imposti da dong ( zeta tra 0 e 1 )
lb=[0.1 0.1 0.1 0.1];
ub=[inf inf inf inf];

%ottimizzazione non lineare con metodo least square ( like Dong )
options = optimoptions(‘lsqnonlin’, ‘Display’, ‘iter’, ‘Algorithm’,’levenberg-marquardt’);
[p_opt, resnorm] = lsqnonlin(@(p) error_function(p,strain,stress,a0), p_initial,lb,ub,options);

%check plot e ricostruzione del modello con i parametri ottimizzati
c = p_opt(1); % c
k1 = p_opt(2); % k1
k2 = p_opt(3); % k2
zeta = p_opt(4); % zeta

for i=1:length(strain)
f = [strain(i) 0 0; %deformation gradient for each value of strain
0 1/sqrt(strain(i)) 0;
0 0 1/sqrt(strain(i))];
C = f .* f’; % Calcola il tensore di Cauchy-Green
prod = a0 .* a0′;
I4 = sum(sum(C .* prod)); % calcolo invariante di C
I = eye(3); % Matrice identità

sigma = 2 * f * (c / 2 * I + k1 * (1 – zeta)^2 * (I4 – 1) * (exp(k2 * ((1 – zeta) * (I4 – 1))^2))) * prod .* f’;
sigma_calculated(i)= sigma(1,1); %componente xx
end

figure(1)
plot (strain,stress);
hold on
plot(strain, sigma_calculated,’r’);
legend (‘experimental’, ‘model’,’Location’, ‘northwest’);

Error function:
function error = error_function(p,strain,stress,a0,k)

sigma_calculated = zeros(length(strain), 1);

for i = 1:length(strain)

f = [strain(i) 0 0; %deformation gradient
0 1/sqrt(strain(i)) 0;
0 0 1/sqrt(strain(i))];
C = f .* f’; % tensore di Cauchy-Green
prod = a0 .* a0′;
I4 = a0′ .* C .* a0; % calcolo IV invariante di C
I = eye(3);
sigma = 2 * f * (p(1) / 2 * I + p(2) * (1 – p(4))^2 * (I4 – 1) * (exp(p(3) * ((1 – p(4)) * (I4 – 1))^2)) * prod) .* f’;
sigma_calculated(i)= sigma(1,1); %componente xx

end
%il calcolo dell’errore va dentro o fuori dal for?
error= sum((sigma_calculated(i) – stress(i))^2); % Somma dei quadrati delle differenze
% disp([‘Iterazione: ‘, num2str(k), ‘ – Errore totale attuale: ‘, num2str(error)]);

endI implemented a constitutive model for the bovine pericardium to be fit with experimental data to derive four model parameters. The code runs, but I have doubts about the correct implementation of the error function. Showing the iterations on the screen, I notice that at certain points the objective function returns nan or inf. I have checked the experimental data and in some cases there are values close to zero, but I don’t think this justifies the fact that the function converges, thus giving me the 4 parameters I am looking for, but without obtaining a decent fitting with the experimental data. Moreover, optimized parameters number 2 and 3 are assigned to lower bounds, while the first optimized parameter is very high. Below is the main and the error function.
Main:
clc; clear all;close all;
load ("data_11122.mat"); % change lot number if necessary
clearvars -except data

%Model requires lambda= x/x0 instead of epsilon: calculate lambda
% from exp data and store in data struct

stress=data(1).stress; %change index to change dogbone if necessary
strain=data(1).strain; %change index to change dogbone if necessary
% stress=stress/max(abs(stress));
% strain=strain/max(abs(strain));
p_initial=[0.5 0.5 0.5 0.5];
a0=[1 0 0];
%vincoli su zeta imposti da dong ( zeta tra 0 e 1 )
lb=[0.1 0.1 0.1 0.1];
ub=[inf inf inf inf];

%ottimizzazione non lineare con metodo least square ( like Dong )
options = optimoptions(‘lsqnonlin’, ‘Display’, ‘iter’, ‘Algorithm’,’levenberg-marquardt’);
[p_opt, resnorm] = lsqnonlin(@(p) error_function(p,strain,stress,a0), p_initial,lb,ub,options);

%check plot e ricostruzione del modello con i parametri ottimizzati
c = p_opt(1); % c
k1 = p_opt(2); % k1
k2 = p_opt(3); % k2
zeta = p_opt(4); % zeta

for i=1:length(strain)
f = [strain(i) 0 0; %deformation gradient for each value of strain
0 1/sqrt(strain(i)) 0;
0 0 1/sqrt(strain(i))];
C = f .* f’; % Calcola il tensore di Cauchy-Green
prod = a0 .* a0′;
I4 = sum(sum(C .* prod)); % calcolo invariante di C
I = eye(3); % Matrice identità

sigma = 2 * f * (c / 2 * I + k1 * (1 – zeta)^2 * (I4 – 1) * (exp(k2 * ((1 – zeta) * (I4 – 1))^2))) * prod .* f’;
sigma_calculated(i)= sigma(1,1); %componente xx
end

figure(1)
plot (strain,stress);
hold on
plot(strain, sigma_calculated,’r’);
legend (‘experimental’, ‘model’,’Location’, ‘northwest’);

Error function:
function error = error_function(p,strain,stress,a0,k)

sigma_calculated = zeros(length(strain), 1);

for i = 1:length(strain)

f = [strain(i) 0 0; %deformation gradient
0 1/sqrt(strain(i)) 0;
0 0 1/sqrt(strain(i))];
C = f .* f’; % tensore di Cauchy-Green
prod = a0 .* a0′;
I4 = a0′ .* C .* a0; % calcolo IV invariante di C
I = eye(3);
sigma = 2 * f * (p(1) / 2 * I + p(2) * (1 – p(4))^2 * (I4 – 1) * (exp(p(3) * ((1 – p(4)) * (I4 – 1))^2)) * prod) .* f’;
sigma_calculated(i)= sigma(1,1); %componente xx

end
%il calcolo dell’errore va dentro o fuori dal for?
error= sum((sigma_calculated(i) – stress(i))^2); % Somma dei quadrati delle differenze
% disp([‘Iterazione: ‘, num2str(k), ‘ – Errore totale attuale: ‘, num2str(error)]);

end I implemented a constitutive model for the bovine pericardium to be fit with experimental data to derive four model parameters. The code runs, but I have doubts about the correct implementation of the error function. Showing the iterations on the screen, I notice that at certain points the objective function returns nan or inf. I have checked the experimental data and in some cases there are values close to zero, but I don’t think this justifies the fact that the function converges, thus giving me the 4 parameters I am looking for, but without obtaining a decent fitting with the experimental data. Moreover, optimized parameters number 2 and 3 are assigned to lower bounds, while the first optimized parameter is very high. Below is the main and the error function.
Main:
clc; clear all;close all;
load ("data_11122.mat"); % change lot number if necessary
clearvars -except data

%Model requires lambda= x/x0 instead of epsilon: calculate lambda
% from exp data and store in data struct

stress=data(1).stress; %change index to change dogbone if necessary
strain=data(1).strain; %change index to change dogbone if necessary
% stress=stress/max(abs(stress));
% strain=strain/max(abs(strain));
p_initial=[0.5 0.5 0.5 0.5];
a0=[1 0 0];
%vincoli su zeta imposti da dong ( zeta tra 0 e 1 )
lb=[0.1 0.1 0.1 0.1];
ub=[inf inf inf inf];

%ottimizzazione non lineare con metodo least square ( like Dong )
options = optimoptions(‘lsqnonlin’, ‘Display’, ‘iter’, ‘Algorithm’,’levenberg-marquardt’);
[p_opt, resnorm] = lsqnonlin(@(p) error_function(p,strain,stress,a0), p_initial,lb,ub,options);

%check plot e ricostruzione del modello con i parametri ottimizzati
c = p_opt(1); % c
k1 = p_opt(2); % k1
k2 = p_opt(3); % k2
zeta = p_opt(4); % zeta

for i=1:length(strain)
f = [strain(i) 0 0; %deformation gradient for each value of strain
0 1/sqrt(strain(i)) 0;
0 0 1/sqrt(strain(i))];
C = f .* f’; % Calcola il tensore di Cauchy-Green
prod = a0 .* a0′;
I4 = sum(sum(C .* prod)); % calcolo invariante di C
I = eye(3); % Matrice identità

sigma = 2 * f * (c / 2 * I + k1 * (1 – zeta)^2 * (I4 – 1) * (exp(k2 * ((1 – zeta) * (I4 – 1))^2))) * prod .* f’;
sigma_calculated(i)= sigma(1,1); %componente xx
end

figure(1)
plot (strain,stress);
hold on
plot(strain, sigma_calculated,’r’);
legend (‘experimental’, ‘model’,’Location’, ‘northwest’);

Error function:
function error = error_function(p,strain,stress,a0,k)

sigma_calculated = zeros(length(strain), 1);

for i = 1:length(strain)

f = [strain(i) 0 0; %deformation gradient
0 1/sqrt(strain(i)) 0;
0 0 1/sqrt(strain(i))];
C = f .* f’; % tensore di Cauchy-Green
prod = a0 .* a0′;
I4 = a0′ .* C .* a0; % calcolo IV invariante di C
I = eye(3);
sigma = 2 * f * (p(1) / 2 * I + p(2) * (1 – p(4))^2 * (I4 – 1) * (exp(p(3) * ((1 – p(4)) * (I4 – 1))^2)) * prod) .* f’;
sigma_calculated(i)= sigma(1,1); %componente xx

end
%il calcolo dell’errore va dentro o fuori dal for?
error= sum((sigma_calculated(i) – stress(i))^2); % Somma dei quadrati delle differenze
% disp([‘Iterazione: ‘, num2str(k), ‘ – Errore totale attuale: ‘, num2str(error)]);

end curve fitting, nonlinear, soft tissue MATLAB Answers — New Questions

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