In the process of reinforcement learning, a problem will be encountered. During the training process, an effective agent will appear. At this time, the training will be finished in advance, but the result of the saved agent running out will be worseIn the process of reinforcement learning, a problem will be encountered. During the training process, an effective agent will appear. At this time, the training will be finished in advance, but the result of the saved agent running out will be worse In the process of reinforcement learning, a problem will be encountered. During the training process, an effective agent will appear. At this time, the training will be finished in advance, but the result of the saved agent running out will be worse problems in reinforcement learning training MATLAB Answers — New Questions

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Dear All
I am in troubling trying to perform a multi-ahead (closed loop) forecasting for a time series. I use Matlab2024 and the "old" command predictAndUpdate does not work with dlnetwork objects. I saw all the possible documentation, theoretically I can manage the question but practically not!. I sum up the excercise. Single univariate time serie (stock price) in LSTM net. No problem with trainnet and predict using historical training and test data. Now I’d like to go out of test data. This is the code
X = (XTest);
T = YTest;
%net2 is a dlnetwork object
offset = length(X);
[Z,state] = predict(net2,X(1:offset)); %predict and update state using test data
net2.State = state;
% five-steps ahead
numPredictionTimeSteps = 5;
Y = (zeros(numPredictionTimeSteps));
Y(1,:) = Z(end); %use the last forecast as starting point for the loop
for t = 2:numPredictionTimeSteps
[Y(:,t),state] = predict(net2,Y(:,t-1));
net2.State = state;
end
%I got:
Error using extractState (line 41)
If the hidden state argument is a matrix, then its number of observations must match the number of observations of the
input data.

I found a similar question on the web for the GRU net. If I restart the net before the loop (as suggested) it works but the forecast is very poor, so I wonder whether the restart can affect such a result. Is there an alternative to reset? I attach the data and the net.
thanks in advanceDear All
I am in troubling trying to perform a multi-ahead (closed loop) forecasting for a time series. I use Matlab2024 and the "old" command predictAndUpdate does not work with dlnetwork objects. I saw all the possible documentation, theoretically I can manage the question but practically not!. I sum up the excercise. Single univariate time serie (stock price) in LSTM net. No problem with trainnet and predict using historical training and test data. Now I’d like to go out of test data. This is the code
X = (XTest);
T = YTest;
%net2 is a dlnetwork object
offset = length(X);
[Z,state] = predict(net2,X(1:offset)); %predict and update state using test data
net2.State = state;
% five-steps ahead
numPredictionTimeSteps = 5;
Y = (zeros(numPredictionTimeSteps));
Y(1,:) = Z(end); %use the last forecast as starting point for the loop
for t = 2:numPredictionTimeSteps
[Y(:,t),state] = predict(net2,Y(:,t-1));
net2.State = state;
end
%I got:
Error using extractState (line 41)
If the hidden state argument is a matrix, then its number of observations must match the number of observations of the
input data.

I found a similar question on the web for the GRU net. If I restart the net before the loop (as suggested) it works but the forecast is very poor, so I wonder whether the restart can affect such a result. Is there an alternative to reset? I attach the data and the net.
thanks in advance Dear All
I am in troubling trying to perform a multi-ahead (closed loop) forecasting for a time series. I use Matlab2024 and the "old" command predictAndUpdate does not work with dlnetwork objects. I saw all the possible documentation, theoretically I can manage the question but practically not!. I sum up the excercise. Single univariate time serie (stock price) in LSTM net. No problem with trainnet and predict using historical training and test data. Now I’d like to go out of test data. This is the code
X = (XTest);
T = YTest;
%net2 is a dlnetwork object
offset = length(X);
[Z,state] = predict(net2,X(1:offset)); %predict and update state using test data
net2.State = state;
% five-steps ahead
numPredictionTimeSteps = 5;
Y = (zeros(numPredictionTimeSteps));
Y(1,:) = Z(end); %use the last forecast as starting point for the loop
for t = 2:numPredictionTimeSteps
[Y(:,t),state] = predict(net2,Y(:,t-1));
net2.State = state;
end
%I got:
Error using extractState (line 41)
If the hidden state argument is a matrix, then its number of observations must match the number of observations of the
input data.

I found a similar question on the web for the GRU net. If I restart the net before the loop (as suggested) it works but the forecast is very poor, so I wonder whether the restart can affect such a result. Is there an alternative to reset? I attach the data and the net.
thanks in advance lstm closed loop MATLAB Answers — New Questions

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When I try to connect my BeagleBone to Matlab/Simulink using Matlab’s instructions (https://de.mathworks.com/help/supportpkg/beaglebone/ug/install-target-for-beaglebone-black-hardware.html) I manage to get until step 6 (under "Complete Setup Tasks"). As soon as I hit start to update the firmware, the wait bar reacts for a second before stopping and showing "Retry" on the button.

Has anyone come across the same problem and was able to solve it?When I try to connect my BeagleBone to Matlab/Simulink using Matlab’s instructions (https://de.mathworks.com/help/supportpkg/beaglebone/ug/install-target-for-beaglebone-black-hardware.html) I manage to get until step 6 (under "Complete Setup Tasks"). As soon as I hit start to update the firmware, the wait bar reacts for a second before stopping and showing "Retry" on the button.

Has anyone come across the same problem and was able to solve it? When I try to connect my BeagleBone to Matlab/Simulink using Matlab’s instructions (https://de.mathworks.com/help/supportpkg/beaglebone/ug/install-target-for-beaglebone-black-hardware.html) I manage to get until step 6 (under "Complete Setup Tasks"). As soon as I hit start to update the firmware, the wait bar reacts for a second before stopping and showing "Retry" on the button.

Has anyone come across the same problem and was able to solve it? beaglebone, embedded coder, linux MATLAB Answers — New Questions

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Good afternoon,
I understand that the topic of NARX networks has been discussed here for over 10 years, but I would like to revisit it.
I attempted to implement a solution for processing data from a sensor in real-time, but unfortunately, I could not find any examples demonstrating how to do this correctly. Most publications on this topic either conclude with a performance evaluation of the network or focus on discussions of the code generated by the application.

Let me share some code:
% targer timeseries
T = num2cell(lfarr(1:8000));
% input timeseries from sensor
X = num2cell(source(1:8000));

The signals are extremely simple – data from the sensor and its smoothed version.

Next, I followed the standard steps outlined in the documentation and various discussions on this forum:
%create narxnet
sh = 7
d1 = [1:sh];
d2 = [1:sh];
narx_net = narxnet(d1,d2,20);
narx_netnet.divideFcn = ‘divideblock’;
(If Greg suddenly comes, tell him that I remember about autocorrelation and am already carefully studying his code from user group :))

% standard data preparation
[Xs,Xi,Ai,Ts] = preparets(narx_net,X,{},T);

% training
rng( ‘default’ )
[narx_net, tr, Ys, Es, Xf, Af ] = train(narx_net,Xs,Ts,Xi,Ai);

% performance evaluation
[Y,Xf,Af] = narx_net(Xs,Xi,Ai);
perf = perform(narx_net,Ts,Y) % perf = 3.4381e-08. I think it’s not bad.

The regression diagram speaks for itself

Next I close the network and simulate signal processing on the remainder of the data that the network has not seen.
Here, I tried to base my approach on the last two paragraphs of the documentation – Following Closed-Loop Simulation with Open-Loop Simulation.
[narx_netc,Xic,Aic] = closeloop(narx_net, Xf, Af);

% number of prediction steps
k = 5;

% A loop that simulates a real-time data flow
for i = 8001:length(lf_arr)
% getting a new value from the sensor
newSensorValue = num2cell(source(i));

% local variable for indexing the prediction array
ii = 1;
% array of predictions
res = [];

% predictions for k steps
for j=i:i+k
[Yc, Xic, Aic] = narx_netc(newSensorValue, Xic, Aic);
% disp([‘Predicted meaning: ‘, num2str(cell2mat(Yc))]);
res(ii) = cell2mat(Yc);
ii = ii+1;
end

% saving results
res1(i-8000,:) = res’;

% form new input and target arrays with new input value and the predicted result
u = [X(2:end),newSensorValue];
y = [T(2:end), {res(1)}];

% repeating initialization cycle for next simulation step
[X1,Xi,Ai,T1] = preparets(narx_net,u,{},y);
[Y,Xio,Aio] = narx_net(X1,Xi,Ai);
[narx_netc,Xic,Aic] = closeloop(narx_net, Xio, Aio);
end

The network starts working, but after some steps its normal operation stops.

I would be immensely grateful for any insights into where my reasoning or actions might have gone astray.
ThanksGood afternoon,
I understand that the topic of NARX networks has been discussed here for over 10 years, but I would like to revisit it.
I attempted to implement a solution for processing data from a sensor in real-time, but unfortunately, I could not find any examples demonstrating how to do this correctly. Most publications on this topic either conclude with a performance evaluation of the network or focus on discussions of the code generated by the application.

Let me share some code:
% targer timeseries
T = num2cell(lfarr(1:8000));
% input timeseries from sensor
X = num2cell(source(1:8000));

The signals are extremely simple – data from the sensor and its smoothed version.

Next, I followed the standard steps outlined in the documentation and various discussions on this forum:
%create narxnet
sh = 7
d1 = [1:sh];
d2 = [1:sh];
narx_net = narxnet(d1,d2,20);
narx_netnet.divideFcn = ‘divideblock’;
(If Greg suddenly comes, tell him that I remember about autocorrelation and am already carefully studying his code from user group :))

% standard data preparation
[Xs,Xi,Ai,Ts] = preparets(narx_net,X,{},T);

% training
rng( ‘default’ )
[narx_net, tr, Ys, Es, Xf, Af ] = train(narx_net,Xs,Ts,Xi,Ai);

% performance evaluation
[Y,Xf,Af] = narx_net(Xs,Xi,Ai);
perf = perform(narx_net,Ts,Y) % perf = 3.4381e-08. I think it’s not bad.

The regression diagram speaks for itself

Next I close the network and simulate signal processing on the remainder of the data that the network has not seen.
Here, I tried to base my approach on the last two paragraphs of the documentation – Following Closed-Loop Simulation with Open-Loop Simulation.
[narx_netc,Xic,Aic] = closeloop(narx_net, Xf, Af);

% number of prediction steps
k = 5;

% A loop that simulates a real-time data flow
for i = 8001:length(lf_arr)
% getting a new value from the sensor
newSensorValue = num2cell(source(i));

% local variable for indexing the prediction array
ii = 1;
% array of predictions
res = [];

% predictions for k steps
for j=i:i+k
[Yc, Xic, Aic] = narx_netc(newSensorValue, Xic, Aic);
% disp([‘Predicted meaning: ‘, num2str(cell2mat(Yc))]);
res(ii) = cell2mat(Yc);
ii = ii+1;
end

% saving results
res1(i-8000,:) = res’;

% form new input and target arrays with new input value and the predicted result
u = [X(2:end),newSensorValue];
y = [T(2:end), {res(1)}];

% repeating initialization cycle for next simulation step
[X1,Xi,Ai,T1] = preparets(narx_net,u,{},y);
[Y,Xio,Aio] = narx_net(X1,Xi,Ai);
[narx_netc,Xic,Aic] = closeloop(narx_net, Xio, Aio);
end

The network starts working, but after some steps its normal operation stops.

I would be immensely grateful for any insights into where my reasoning or actions might have gone astray.
Thanks Good afternoon,
I understand that the topic of NARX networks has been discussed here for over 10 years, but I would like to revisit it.
I attempted to implement a solution for processing data from a sensor in real-time, but unfortunately, I could not find any examples demonstrating how to do this correctly. Most publications on this topic either conclude with a performance evaluation of the network or focus on discussions of the code generated by the application.

Let me share some code:
% targer timeseries
T = num2cell(lfarr(1:8000));
% input timeseries from sensor
X = num2cell(source(1:8000));

The signals are extremely simple – data from the sensor and its smoothed version.

Next, I followed the standard steps outlined in the documentation and various discussions on this forum:
%create narxnet
sh = 7
d1 = [1:sh];
d2 = [1:sh];
narx_net = narxnet(d1,d2,20);
narx_netnet.divideFcn = ‘divideblock’;
(If Greg suddenly comes, tell him that I remember about autocorrelation and am already carefully studying his code from user group :))

% standard data preparation
[Xs,Xi,Ai,Ts] = preparets(narx_net,X,{},T);

% training
rng( ‘default’ )
[narx_net, tr, Ys, Es, Xf, Af ] = train(narx_net,Xs,Ts,Xi,Ai);

% performance evaluation
[Y,Xf,Af] = narx_net(Xs,Xi,Ai);
perf = perform(narx_net,Ts,Y) % perf = 3.4381e-08. I think it’s not bad.

The regression diagram speaks for itself

Next I close the network and simulate signal processing on the remainder of the data that the network has not seen.
Here, I tried to base my approach on the last two paragraphs of the documentation – Following Closed-Loop Simulation with Open-Loop Simulation.
[narx_netc,Xic,Aic] = closeloop(narx_net, Xf, Af);

% number of prediction steps
k = 5;

% A loop that simulates a real-time data flow
for i = 8001:length(lf_arr)
% getting a new value from the sensor
newSensorValue = num2cell(source(i));

% local variable for indexing the prediction array
ii = 1;
% array of predictions
res = [];

% predictions for k steps
for j=i:i+k
[Yc, Xic, Aic] = narx_netc(newSensorValue, Xic, Aic);
% disp([‘Predicted meaning: ‘, num2str(cell2mat(Yc))]);
res(ii) = cell2mat(Yc);
ii = ii+1;
end

% saving results
res1(i-8000,:) = res’;

% form new input and target arrays with new input value and the predicted result
u = [X(2:end),newSensorValue];
y = [T(2:end), {res(1)}];

% repeating initialization cycle for next simulation step
[X1,Xi,Ai,T1] = preparets(narx_net,u,{},y);
[Y,Xio,Aio] = narx_net(X1,Xi,Ai);
[narx_netc,Xic,Aic] = closeloop(narx_net, Xio, Aio);
end

The network starts working, but after some steps its normal operation stops.

I would be immensely grateful for any insights into where my reasoning or actions might have gone astray.
Thanks narx network MATLAB Answers — New Questions

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Hello, I’ve encountered a problem with NetCDF library, I would be very grateful for any tips how to solve it.
I’m trying to create a netcdffile and write variables in it. But for some reason after creating a file I can’t reach it anyhow (nccreate, ncdisp are getting the same error):
ncid = netcdf.create(‘F:GLORYS_dayARCGLORYS_surf.nc’,’NETCDF4′);

nccreate(‘F:GLORYS_dayARCGLORYS_surf.nc’,’Lat’,’Dimensions’,{‘Lat’ 205});

Error using matlab.internal.imagesci.netcdflib
The NetCDF library encountered an error during execution of ‘open’ function – ‘Unknown file format (NC_ENOTNC)’.

Error in netcdf.open (line 77)
[varargout{:}] = matlab.internal.imagesci.netcdflib(‘open’, …

Error in internal.matlab.imagesci.nc/openToAppend (line 1250)
this.ncRootid = netcdf.open(this.Filename,’WRITE’);

Error in internal.matlab.imagesci.nc (line 126)
this.openToAppend();

Error in nccreate (line 159)
ncObj = internal.matlab.imagesci.nc(ncFile,’a’, formatStr);

Error in export_to_netcdf (line 10)
nccreate(‘F:GLORYS_dayARCGLORYS_surf.nc’,’Lat’,’Dimensions’,{‘Lat’ 205});Hello, I’ve encountered a problem with NetCDF library, I would be very grateful for any tips how to solve it.
I’m trying to create a netcdffile and write variables in it. But for some reason after creating a file I can’t reach it anyhow (nccreate, ncdisp are getting the same error):
ncid = netcdf.create(‘F:GLORYS_dayARCGLORYS_surf.nc’,’NETCDF4′);

nccreate(‘F:GLORYS_dayARCGLORYS_surf.nc’,’Lat’,’Dimensions’,{‘Lat’ 205});

Error using matlab.internal.imagesci.netcdflib
The NetCDF library encountered an error during execution of ‘open’ function – ‘Unknown file format (NC_ENOTNC)’.

Error in netcdf.open (line 77)
[varargout{:}] = matlab.internal.imagesci.netcdflib(‘open’, …

Error in internal.matlab.imagesci.nc/openToAppend (line 1250)
this.ncRootid = netcdf.open(this.Filename,’WRITE’);

Error in internal.matlab.imagesci.nc (line 126)
this.openToAppend();

Error in nccreate (line 159)
ncObj = internal.matlab.imagesci.nc(ncFile,’a’, formatStr);

Error in export_to_netcdf (line 10)
nccreate(‘F:GLORYS_dayARCGLORYS_surf.nc’,’Lat’,’Dimensions’,{‘Lat’ 205}); Hello, I’ve encountered a problem with NetCDF library, I would be very grateful for any tips how to solve it.
I’m trying to create a netcdffile and write variables in it. But for some reason after creating a file I can’t reach it anyhow (nccreate, ncdisp are getting the same error):
ncid = netcdf.create(‘F:GLORYS_dayARCGLORYS_surf.nc’,’NETCDF4′);

nccreate(‘F:GLORYS_dayARCGLORYS_surf.nc’,’Lat’,’Dimensions’,{‘Lat’ 205});

Error using matlab.internal.imagesci.netcdflib
The NetCDF library encountered an error during execution of ‘open’ function – ‘Unknown file format (NC_ENOTNC)’.

Error in netcdf.open (line 77)
[varargout{:}] = matlab.internal.imagesci.netcdflib(‘open’, …

Error in internal.matlab.imagesci.nc/openToAppend (line 1250)
this.ncRootid = netcdf.open(this.Filename,’WRITE’);

Error in internal.matlab.imagesci.nc (line 126)
this.openToAppend();

Error in nccreate (line 159)
ncObj = internal.matlab.imagesci.nc(ncFile,’a’, formatStr);

Error in export_to_netcdf (line 10)
nccreate(‘F:GLORYS_dayARCGLORYS_surf.nc’,’Lat’,’Dimensions’,{‘Lat’ 205}); netcdf MATLAB Answers — New Questions

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