I want to make the dropdown content of one system object parameter based on the value of another parameter. In other words, Timer 1 may support options A, B and C, while Timer 2 would only support options A and B. I can do this in a standard subsystem block mask by modifying the option parameter dropdown content on the callback for the timer parameter. MATLAB system objects only seem to support defining dropdown content for their parameters statically. Is this possible?I want to make the dropdown content of one system object parameter based on the value of another parameter. In other words, Timer 1 may support options A, B and C, while Timer 2 would only support options A and B. I can do this in a standard subsystem block mask by modifying the option parameter dropdown content on the callback for the timer parameter. MATLAB system objects only seem to support defining dropdown content for their parameters statically. Is this possible? I want to make the dropdown content of one system object parameter based on the value of another parameter. In other words, Timer 1 may support options A, B and C, while Timer 2 would only support options A and B. I can do this in a standard subsystem block mask by modifying the option parameter dropdown content on the callback for the timer parameter. MATLAB system objects only seem to support defining dropdown content for their parameters statically. Is this possible? matlab system objects MATLAB Answers — New Questions

​

Hello! I’m trying to get a CNN up and running and I think I’m almost there, but I’m still running into a few errors. What I would like is to have a series of 1D convolutions with a featureInputLayer, but those throw the following error:
Caused by:
Layer ‘conv1d1’: Input data must have one spatial dimension only, one temporal dimension only, or one of each. Instead, it
has 0 spatial dimensions and 0 temporal dimensions.
According to https://www.mathworks.com/matlabcentral/answers/1747170-error-on-convolutional-layer-s-input-data-has-0-spatial-dimensions-and-0-temporal-dimensions the workaround is to reformat the CNN to a conv2d using N x 1 "images." So, I’ve tried that and now I have a new and interesting problem:
Error using trainnet (line 46)
Number of channels in predictions (3) must match the number of channels in the targets (1).

Error in convNet_1_edits (line 97)
[trainedNet, trainInfo]=trainnet(masterTrain,net,’mse’,options);
This problem has been approached several times before (https://www.mathworks.com/matlabcentral/answers/2123216-error-in-deep-learning-classification-code/?s_tid=ans_lp_feed_leaf and others) but none of them that I’ve found have used fully connected layers. For reference, my CNN is the following:
layers = [
imageInputLayer(nFeatures, "name", "input");
convolution2dLayer(f1Size, numFilters1, ‘padding’, ‘same’,…
"name", "conv1")
batchNormalizationLayer();
reluLayer();

convolution2dLayer(f1Size, numFilters2, "padding", "same",…
‘numchannels’, numFilters1, ‘name’, ‘conv2’)
batchNormalizationLayer();
reluLayer();
maxPooling2dLayer([1, 3]);

convolution2dLayer(f1Size, numFilters3, "padding", "same",…
‘numchannels’, numFilters2, ‘name’, ‘conv3’)
batchNormalizationLayer();
reluLayer();
maxPooling2dLayer([1, 5]);

fullyConnectedLayer(60, ‘name’, ‘fc1’)
reluLayer()
fullyConnectedLayer(30, ‘name’, ‘fc2’)
reluLayer()
fullyConnectedLayer(15, ‘name’, ‘fc3’)
reluLayer()
fullyConnectedLayer(3, ‘name’, ‘fc4’)
% regressionLayer()
];

net = dlnetwork;
net = addLayers(net, layers);

And I am using trainnetwork and a datastore. The output of read(ds) produces the following:
read(masterTest)

ans =

1×4 cell array

{1×1341 double} {[0.6500]} {[6.8000e-07]} {[0.0250]}

Where I have a 1 x 1341 set of features being used to predict three outputs. I thought the three neurons in my final fully connected layer would be the regression outputs, but there seems to be a mismatch in the number of predictions and number of targets. How can I align the number of predictions and targets when using regression in FC layers?Hello! I’m trying to get a CNN up and running and I think I’m almost there, but I’m still running into a few errors. What I would like is to have a series of 1D convolutions with a featureInputLayer, but those throw the following error:
Caused by:
Layer ‘conv1d1’: Input data must have one spatial dimension only, one temporal dimension only, or one of each. Instead, it
has 0 spatial dimensions and 0 temporal dimensions.
According to https://www.mathworks.com/matlabcentral/answers/1747170-error-on-convolutional-layer-s-input-data-has-0-spatial-dimensions-and-0-temporal-dimensions the workaround is to reformat the CNN to a conv2d using N x 1 "images." So, I’ve tried that and now I have a new and interesting problem:
Error using trainnet (line 46)
Number of channels in predictions (3) must match the number of channels in the targets (1).

Error in convNet_1_edits (line 97)
[trainedNet, trainInfo]=trainnet(masterTrain,net,’mse’,options);
This problem has been approached several times before (https://www.mathworks.com/matlabcentral/answers/2123216-error-in-deep-learning-classification-code/?s_tid=ans_lp_feed_leaf and others) but none of them that I’ve found have used fully connected layers. For reference, my CNN is the following:
layers = [
imageInputLayer(nFeatures, "name", "input");
convolution2dLayer(f1Size, numFilters1, ‘padding’, ‘same’,…
"name", "conv1")
batchNormalizationLayer();
reluLayer();

convolution2dLayer(f1Size, numFilters2, "padding", "same",…
‘numchannels’, numFilters1, ‘name’, ‘conv2’)
batchNormalizationLayer();
reluLayer();
maxPooling2dLayer([1, 3]);

convolution2dLayer(f1Size, numFilters3, "padding", "same",…
‘numchannels’, numFilters2, ‘name’, ‘conv3’)
batchNormalizationLayer();
reluLayer();
maxPooling2dLayer([1, 5]);

fullyConnectedLayer(60, ‘name’, ‘fc1’)
reluLayer()
fullyConnectedLayer(30, ‘name’, ‘fc2’)
reluLayer()
fullyConnectedLayer(15, ‘name’, ‘fc3’)
reluLayer()
fullyConnectedLayer(3, ‘name’, ‘fc4’)
% regressionLayer()
];

net = dlnetwork;
net = addLayers(net, layers);

And I am using trainnetwork and a datastore. The output of read(ds) produces the following:
read(masterTest)

ans =

1×4 cell array

{1×1341 double} {[0.6500]} {[6.8000e-07]} {[0.0250]}

Where I have a 1 x 1341 set of features being used to predict three outputs. I thought the three neurons in my final fully connected layer would be the regression outputs, but there seems to be a mismatch in the number of predictions and number of targets. How can I align the number of predictions and targets when using regression in FC layers? Hello! I’m trying to get a CNN up and running and I think I’m almost there, but I’m still running into a few errors. What I would like is to have a series of 1D convolutions with a featureInputLayer, but those throw the following error:
Caused by:
Layer ‘conv1d1’: Input data must have one spatial dimension only, one temporal dimension only, or one of each. Instead, it
has 0 spatial dimensions and 0 temporal dimensions.
According to https://www.mathworks.com/matlabcentral/answers/1747170-error-on-convolutional-layer-s-input-data-has-0-spatial-dimensions-and-0-temporal-dimensions the workaround is to reformat the CNN to a conv2d using N x 1 "images." So, I’ve tried that and now I have a new and interesting problem:
Error using trainnet (line 46)
Number of channels in predictions (3) must match the number of channels in the targets (1).

Error in convNet_1_edits (line 97)
[trainedNet, trainInfo]=trainnet(masterTrain,net,’mse’,options);
This problem has been approached several times before (https://www.mathworks.com/matlabcentral/answers/2123216-error-in-deep-learning-classification-code/?s_tid=ans_lp_feed_leaf and others) but none of them that I’ve found have used fully connected layers. For reference, my CNN is the following:
layers = [
imageInputLayer(nFeatures, "name", "input");
convolution2dLayer(f1Size, numFilters1, ‘padding’, ‘same’,…
"name", "conv1")
batchNormalizationLayer();
reluLayer();

convolution2dLayer(f1Size, numFilters2, "padding", "same",…
‘numchannels’, numFilters1, ‘name’, ‘conv2’)
batchNormalizationLayer();
reluLayer();
maxPooling2dLayer([1, 3]);

convolution2dLayer(f1Size, numFilters3, "padding", "same",…
‘numchannels’, numFilters2, ‘name’, ‘conv3’)
batchNormalizationLayer();
reluLayer();
maxPooling2dLayer([1, 5]);

fullyConnectedLayer(60, ‘name’, ‘fc1’)
reluLayer()
fullyConnectedLayer(30, ‘name’, ‘fc2’)
reluLayer()
fullyConnectedLayer(15, ‘name’, ‘fc3’)
reluLayer()
fullyConnectedLayer(3, ‘name’, ‘fc4’)
% regressionLayer()
];

net = dlnetwork;
net = addLayers(net, layers);

And I am using trainnetwork and a datastore. The output of read(ds) produces the following:
read(masterTest)

ans =

1×4 cell array

{1×1341 double} {[0.6500]} {[6.8000e-07]} {[0.0250]}

Where I have a 1 x 1341 set of features being used to predict three outputs. I thought the three neurons in my final fully connected layer would be the regression outputs, but there seems to be a mismatch in the number of predictions and number of targets. How can I align the number of predictions and targets when using regression in FC layers? convolution, cnn, regression, trainnet MATLAB Answers — New Questions

​

Hello.
I have a series of photos of the seven-digit display (below please find the example of such photo). I want to apply OCR to extract the information from each consecutive frame. Generally, the methods works quite fine provided that the image is distinct. Hovewer, at the preprocessing stage there is a need to binarize the image. The problem lies in a fact that there are some reflections in the image. I spent a significant amount of time and tried a lot of combinations and various functions (e.g. adaptive thresholding, histograms) to obtain the best possible performance. Is there any reasonable method for obtaining a nice set of digits without artifacts? Unfortunately there is no way to repeat the experiments in better conditions and remove the reflections at acqusition stage.
Thank you kindly in advance for any useful suggestions.Hello.
I have a series of photos of the seven-digit display (below please find the example of such photo). I want to apply OCR to extract the information from each consecutive frame. Generally, the methods works quite fine provided that the image is distinct. Hovewer, at the preprocessing stage there is a need to binarize the image. The problem lies in a fact that there are some reflections in the image. I spent a significant amount of time and tried a lot of combinations and various functions (e.g. adaptive thresholding, histograms) to obtain the best possible performance. Is there any reasonable method for obtaining a nice set of digits without artifacts? Unfortunately there is no way to repeat the experiments in better conditions and remove the reflections at acqusition stage.
Thank you kindly in advance for any useful suggestions. Hello.
I have a series of photos of the seven-digit display (below please find the example of such photo). I want to apply OCR to extract the information from each consecutive frame. Generally, the methods works quite fine provided that the image is distinct. Hovewer, at the preprocessing stage there is a need to binarize the image. The problem lies in a fact that there are some reflections in the image. I spent a significant amount of time and tried a lot of combinations and various functions (e.g. adaptive thresholding, histograms) to obtain the best possible performance. Is there any reasonable method for obtaining a nice set of digits without artifacts? Unfortunately there is no way to repeat the experiments in better conditions and remove the reflections at acqusition stage.
Thank you kindly in advance for any useful suggestions. binarize, thresholding, image processing MATLAB Answers — New Questions

​

I am new to reinforcement learning and have run the programs given in online ramp course of reinforcement learning. In the randomstart function I made only a single change as given in the code below but the program is giving the attached image. I have seen the documentation, where all examples are given with random numbers. But I all inputvariables i.e. x0, y0, theta0, v0, and w0 to be fix and should be picked from already stored vectors, When I tried to fix only with single variable x0, the program is generating an error. How can I fix it?
function in = randomstart(in)
mdl = "whrobot";
a=0.5;
% in = setVariable(in,"x0",((-1)^randi([0 1]))*(2.5 + 3.5*rand),"Workspace",mdl);
in = setVariable(in,"x0",a,"Workspace",mdl);
in = setVariable(in,"y0",2.6 + 3.4*rand,"Workspace",mdl);
in = setVariable(in,"theta0",pi*(2*rand-1),"Workspace",mdl);
in = setVariable(in,"v0",randn/3,"Workspace",mdl);
in = setVariable(in,"w0",randn/3,"Workspace",mdl);
disp(x0)
endI am new to reinforcement learning and have run the programs given in online ramp course of reinforcement learning. In the randomstart function I made only a single change as given in the code below but the program is giving the attached image. I have seen the documentation, where all examples are given with random numbers. But I all inputvariables i.e. x0, y0, theta0, v0, and w0 to be fix and should be picked from already stored vectors, When I tried to fix only with single variable x0, the program is generating an error. How can I fix it?
function in = randomstart(in)
mdl = "whrobot";
a=0.5;
% in = setVariable(in,"x0",((-1)^randi([0 1]))*(2.5 + 3.5*rand),"Workspace",mdl);
in = setVariable(in,"x0",a,"Workspace",mdl);
in = setVariable(in,"y0",2.6 + 3.4*rand,"Workspace",mdl);
in = setVariable(in,"theta0",pi*(2*rand-1),"Workspace",mdl);
in = setVariable(in,"v0",randn/3,"Workspace",mdl);
in = setVariable(in,"w0",randn/3,"Workspace",mdl);
disp(x0)
end I am new to reinforcement learning and have run the programs given in online ramp course of reinforcement learning. In the randomstart function I made only a single change as given in the code below but the program is giving the attached image. I have seen the documentation, where all examples are given with random numbers. But I all inputvariables i.e. x0, y0, theta0, v0, and w0 to be fix and should be picked from already stored vectors, When I tried to fix only with single variable x0, the program is generating an error. How can I fix it?
function in = randomstart(in)
mdl = "whrobot";
a=0.5;
% in = setVariable(in,"x0",((-1)^randi([0 1]))*(2.5 + 3.5*rand),"Workspace",mdl);
in = setVariable(in,"x0",a,"Workspace",mdl);
in = setVariable(in,"y0",2.6 + 3.4*rand,"Workspace",mdl);
in = setVariable(in,"theta0",pi*(2*rand-1),"Workspace",mdl);
in = setVariable(in,"v0",randn/3,"Workspace",mdl);
in = setVariable(in,"w0",randn/3,"Workspace",mdl);
disp(x0)
end reinforcement learning, input, env MATLAB Answers — New Questions

​

I would like to find an approach to define a path of vehicle in path-length coordinate.

As input I have an array of Cartesian coordinates (X, Y). I need to convert it to some function (object) which allow me to get a curvature of path, X, Y as functions of path length.
Also there is a issue that I have a closed path like the following:

<</matlabcentral/answers/uploaded_files/54861/closed_lap.png>>

What can I use for such task?I would like to find an approach to define a path of vehicle in path-length coordinate.

As input I have an array of Cartesian coordinates (X, Y). I need to convert it to some function (object) which allow me to get a curvature of path, X, Y as functions of path length.
Also there is a issue that I have a closed path like the following:

<</matlabcentral/answers/uploaded_files/54861/closed_lap.png>>

What can I use for such task? I would like to find an approach to define a path of vehicle in path-length coordinate.

As input I have an array of Cartesian coordinates (X, Y). I need to convert it to some function (object) which allow me to get a curvature of path, X, Y as functions of path length.
Also there is a issue that I have a closed path like the following:

<</matlabcentral/answers/uploaded_files/54861/closed_lap.png>>

What can I use for such task? spline, path MATLAB Answers — New Questions

​

I am a newcomer to matlab,I want to use the Galerkin method with the hat function as the set of basis functions to calculate the solution to the linear two-point BVP :

The hat knots are evenly distributed with the interval h = 1/20 and 1/40.
Compare the results to those of the exact solution, , to evaluate the order of accuracy using
the absolute errors for the two knot intervals.
Hope someone can teach or guide me how to do it .I am a newcomer to matlab,I want to use the Galerkin method with the hat function as the set of basis functions to calculate the solution to the linear two-point BVP :

The hat knots are evenly distributed with the interval h = 1/20 and 1/40.
Compare the results to those of the exact solution, , to evaluate the order of accuracy using
the absolute errors for the two knot intervals.
Hope someone can teach or guide me how to do it . I am a newcomer to matlab,I want to use the Galerkin method with the hat function as the set of basis functions to calculate the solution to the linear two-point BVP :

The hat knots are evenly distributed with the interval h = 1/20 and 1/40.
Compare the results to those of the exact solution, , to evaluate the order of accuracy using
the absolute errors for the two knot intervals.
Hope someone can teach or guide me how to do it . galerkin, hat function MATLAB Answers — New Questions

​

Hi all,

I have a question regarding matrix manipulation in MATLAB.

My scenario is as follows:

I have a set of GPS coordinates, which I have converted into relative meters using an algorithm. These coordinates correspond to locations on a farm which I have gathered data for using a video camera, and roughly correspond to a standard "go up one row, go down the next" path.

For each coordinate, I also have an image at that coordinate, which I am using to identify the location of weeds.

From the GPS coordinates, which are simple X-Y points, I can generate a matrix. The matrix would likely be a roughly 2000×2000 size matrix, where a cell would have a value of 1 if there was a GPS point identifying that the tractor had been on that spot.

The images are 800x600x3 color images.

What I want to be able to do, is take the images, and, using the matrix I made from the GPS coordinates, somehow combine all images together into one large image.

If the images were distinct, then this would not be as big of an issue, as I could generate an 800x600x3 matrix at each cell of the 2000×2000 matrix. This would be a rather large matrix, however, it would accomplish the task.

However, the GPS coordinates are such that an image might overlap to a certain extent with the images adjacent to it.

Can anyone suggest any ways I can accomplish what I am trying to do? The end result simply needs to be a large image I can look at, which will show me the entirety of my farm land, using the images which I have taken.Hi all,

I have a question regarding matrix manipulation in MATLAB.

My scenario is as follows:

I have a set of GPS coordinates, which I have converted into relative meters using an algorithm. These coordinates correspond to locations on a farm which I have gathered data for using a video camera, and roughly correspond to a standard "go up one row, go down the next" path.

For each coordinate, I also have an image at that coordinate, which I am using to identify the location of weeds.

From the GPS coordinates, which are simple X-Y points, I can generate a matrix. The matrix would likely be a roughly 2000×2000 size matrix, where a cell would have a value of 1 if there was a GPS point identifying that the tractor had been on that spot.

The images are 800x600x3 color images.

What I want to be able to do, is take the images, and, using the matrix I made from the GPS coordinates, somehow combine all images together into one large image.

If the images were distinct, then this would not be as big of an issue, as I could generate an 800x600x3 matrix at each cell of the 2000×2000 matrix. This would be a rather large matrix, however, it would accomplish the task.

However, the GPS coordinates are such that an image might overlap to a certain extent with the images adjacent to it.

Can anyone suggest any ways I can accomplish what I am trying to do? The end result simply needs to be a large image I can look at, which will show me the entirety of my farm land, using the images which I have taken. Hi all,

I have a question regarding matrix manipulation in MATLAB.

My scenario is as follows:

I have a set of GPS coordinates, which I have converted into relative meters using an algorithm. These coordinates correspond to locations on a farm which I have gathered data for using a video camera, and roughly correspond to a standard "go up one row, go down the next" path.

For each coordinate, I also have an image at that coordinate, which I am using to identify the location of weeds.

From the GPS coordinates, which are simple X-Y points, I can generate a matrix. The matrix would likely be a roughly 2000×2000 size matrix, where a cell would have a value of 1 if there was a GPS point identifying that the tractor had been on that spot.

The images are 800x600x3 color images.

What I want to be able to do, is take the images, and, using the matrix I made from the GPS coordinates, somehow combine all images together into one large image.

If the images were distinct, then this would not be as big of an issue, as I could generate an 800x600x3 matrix at each cell of the 2000×2000 matrix. This would be a rather large matrix, however, it would accomplish the task.

However, the GPS coordinates are such that an image might overlap to a certain extent with the images adjacent to it.

Can anyone suggest any ways I can accomplish what I am trying to do? The end result simply needs to be a large image I can look at, which will show me the entirety of my farm land, using the images which I have taken. matrix manipulation, image processing MATLAB Answers — New Questions

​

May I ask how to use MATLAB code to build an ECA module? The ECA module can refer to this paper: ECA Net: Efficient Channel Attention for Deep Convolutional Neural Networks.
Paper address: https://arxiv.org/abs/1910.03151May I ask how to use MATLAB code to build an ECA module? The ECA module can refer to this paper: ECA Net: Efficient Channel Attention for Deep Convolutional Neural Networks.
Paper address: https://arxiv.org/abs/1910.03151 May I ask how to use MATLAB code to build an ECA module? The ECA module can refer to this paper: ECA Net: Efficient Channel Attention for Deep Convolutional Neural Networks.
Paper address: https://arxiv.org/abs/1910.03151 eca-net, attention mechanism MATLAB Answers — New Questions

​

I am currently trying to find the global minimum for a strain-energy-function ("Holzapfel-Model") and I am running into multiple problems:
The SEF has the form
With
We can calculate , where
We want to determine the minimum of the least-square function

My solution was to put all these equations into one long one:
fun = @(x) sum((sigma_11 – (lambda_1.^2 – lambda_2.^2 .* lambda_1.^2).*x(1) + 2 .*lambda_1.^2 .*cos(x(4))^2 .* (2.*x(2).*((lambda_1.^2 .* cos(x(4))^2 + lambda_2.^2 .* sin(x(4))^2) – 1) .* exp(x(2).*((lambda_1.^2 .* cos(x(4))^2 + lambda_2.^2 .* sin(x(4))^2)-1).^2))).^2 + (sigma_22 – (lambda_2.^2 – lambda_2.^2 .* lambda_1.^2).*x(1) + 2 .*lambda_2.^2 .*sin(x(4))^2 .* (2.*x(2).*((lambda_1.^2 .* cos(x(4))^2 + lambda_2.^2 .* sin(x(4))^2) – 1) .* exp(x(2).*((lambda_1.^2 .* cos(x(4))^2 + lambda_2.^2 .* sin(x(4))^2)-1).^2))).^2)

and then use the following parameters
x0 = [15,500,12,0.75*pi];
A = [];
b = [];
Aeq = [];
beq = [];
lb = [0,0,0,0];
ub = [inf, inf, inf, pi];
chi = fmincon(fun, x0,A,b,Aeq,beq,lb,ub,nonlcon)
function [c,ceq] = nonlcon(x,lambda_1,lambda_2)
c =1-(lambda_1.^2 .* cos(x(4)).^2 + lambda_2.^2 * sin(x(4)).^2) ;
ceq = [];
end
With these parameters, I can somewhat get close to my data points.
Now my questions:
I don’t think I understood c,ceq correctly. I used c to account for the constraint on I4, but I’m not sure if this was the right way to do it.
With the initial guess for x0, I can get close but it never seems to approach my curve nearly enough. How do I know if I have a good starting guess, and is fmincon even the right approach for this problem.
I have multiple data sets, for different stretch ratios (lambda_1:lambda_2: 1-1, 1-0.75, 0.75-1, 1-0.5,0.5-1) and since they are the same sample, I would like to use those datas to get one set of parameters for all of them. I tried to put all my data into a single vector, (1:30 would be the first data set, 31:^60 the second,…). This does not seem to work well. Should I find the solution for just one curve and than try to average over the parameters? As you guys can see, I am doing this parameter evaluation thing the first time ever and I would greatly appreciate help.I am currently trying to find the global minimum for a strain-energy-function ("Holzapfel-Model") and I am running into multiple problems:
The SEF has the form
With
We can calculate , where
We want to determine the minimum of the least-square function

My solution was to put all these equations into one long one:
fun = @(x) sum((sigma_11 – (lambda_1.^2 – lambda_2.^2 .* lambda_1.^2).*x(1) + 2 .*lambda_1.^2 .*cos(x(4))^2 .* (2.*x(2).*((lambda_1.^2 .* cos(x(4))^2 + lambda_2.^2 .* sin(x(4))^2) – 1) .* exp(x(2).*((lambda_1.^2 .* cos(x(4))^2 + lambda_2.^2 .* sin(x(4))^2)-1).^2))).^2 + (sigma_22 – (lambda_2.^2 – lambda_2.^2 .* lambda_1.^2).*x(1) + 2 .*lambda_2.^2 .*sin(x(4))^2 .* (2.*x(2).*((lambda_1.^2 .* cos(x(4))^2 + lambda_2.^2 .* sin(x(4))^2) – 1) .* exp(x(2).*((lambda_1.^2 .* cos(x(4))^2 + lambda_2.^2 .* sin(x(4))^2)-1).^2))).^2)

and then use the following parameters
x0 = [15,500,12,0.75*pi];
A = [];
b = [];
Aeq = [];
beq = [];
lb = [0,0,0,0];
ub = [inf, inf, inf, pi];
chi = fmincon(fun, x0,A,b,Aeq,beq,lb,ub,nonlcon)
function [c,ceq] = nonlcon(x,lambda_1,lambda_2)
c =1-(lambda_1.^2 .* cos(x(4)).^2 + lambda_2.^2 * sin(x(4)).^2) ;
ceq = [];
end
With these parameters, I can somewhat get close to my data points.
Now my questions:
I don’t think I understood c,ceq correctly. I used c to account for the constraint on I4, but I’m not sure if this was the right way to do it.
With the initial guess for x0, I can get close but it never seems to approach my curve nearly enough. How do I know if I have a good starting guess, and is fmincon even the right approach for this problem.
I have multiple data sets, for different stretch ratios (lambda_1:lambda_2: 1-1, 1-0.75, 0.75-1, 1-0.5,0.5-1) and since they are the same sample, I would like to use those datas to get one set of parameters for all of them. I tried to put all my data into a single vector, (1:30 would be the first data set, 31:^60 the second,…). This does not seem to work well. Should I find the solution for just one curve and than try to average over the parameters? As you guys can see, I am doing this parameter evaluation thing the first time ever and I would greatly appreciate help. I am currently trying to find the global minimum for a strain-energy-function ("Holzapfel-Model") and I am running into multiple problems:
The SEF has the form
With
We can calculate , where
We want to determine the minimum of the least-square function

My solution was to put all these equations into one long one:
fun = @(x) sum((sigma_11 – (lambda_1.^2 – lambda_2.^2 .* lambda_1.^2).*x(1) + 2 .*lambda_1.^2 .*cos(x(4))^2 .* (2.*x(2).*((lambda_1.^2 .* cos(x(4))^2 + lambda_2.^2 .* sin(x(4))^2) – 1) .* exp(x(2).*((lambda_1.^2 .* cos(x(4))^2 + lambda_2.^2 .* sin(x(4))^2)-1).^2))).^2 + (sigma_22 – (lambda_2.^2 – lambda_2.^2 .* lambda_1.^2).*x(1) + 2 .*lambda_2.^2 .*sin(x(4))^2 .* (2.*x(2).*((lambda_1.^2 .* cos(x(4))^2 + lambda_2.^2 .* sin(x(4))^2) – 1) .* exp(x(2).*((lambda_1.^2 .* cos(x(4))^2 + lambda_2.^2 .* sin(x(4))^2)-1).^2))).^2)

and then use the following parameters
x0 = [15,500,12,0.75*pi];
A = [];
b = [];
Aeq = [];
beq = [];
lb = [0,0,0,0];
ub = [inf, inf, inf, pi];
chi = fmincon(fun, x0,A,b,Aeq,beq,lb,ub,nonlcon)
function [c,ceq] = nonlcon(x,lambda_1,lambda_2)
c =1-(lambda_1.^2 .* cos(x(4)).^2 + lambda_2.^2 * sin(x(4)).^2) ;
ceq = [];
end
With these parameters, I can somewhat get close to my data points.
Now my questions:
I don’t think I understood c,ceq correctly. I used c to account for the constraint on I4, but I’m not sure if this was the right way to do it.
With the initial guess for x0, I can get close but it never seems to approach my curve nearly enough. How do I know if I have a good starting guess, and is fmincon even the right approach for this problem.
I have multiple data sets, for different stretch ratios (lambda_1:lambda_2: 1-1, 1-0.75, 0.75-1, 1-0.5,0.5-1) and since they are the same sample, I would like to use those datas to get one set of parameters for all of them. I tried to put all my data into a single vector, (1:30 would be the first data set, 31:^60 the second,…). This does not seem to work well. Should I find the solution for just one curve and than try to average over the parameters? As you guys can see, I am doing this parameter evaluation thing the first time ever and I would greatly appreciate help. fmincon, nonlinear, curve fitting, optimization MATLAB Answers — New Questions

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Hello,
it should be a ridiculously trivial task, but I have to admit I’ve been stuck on it for a few months. Sadly, I’m not very good at Python either, so I’m coming here.

Assume that I have some circuit like the one below:
I want to read and parse a netlist such that I create a digraph object, which can later be used for testing subgraphs being a spanning tree and alike graph theoretic features. Prsing a netlist posses no difficulty, but it looks like the digraph function does not care about the order in my input cells and when I plot the graph, it is labeled wrongly.
I have spent weeks on it with no result. Can you see a easy solution how to turn it into a graph object and plot it accordingly?
Code below produces obvisouly wrong plot, for instance resistors, while the topoogy seems to be idnetified correctly. Edges/Nodes are mislabeled.

clear
close all
clc

netlist = {
‘R1 N001 0 R’;
‘R2 N002 N001 R’;
‘R3 0 N002 R’;
‘C1 N002 N001 C’;
‘C2 N001 0 C’;
‘C3 N002 0 C’;
‘L1 N002 N001 L’;
‘L2 0 N001 L’;
‘L3 0 N002 L’
};
elements = {};
sourceNodes = {};
targetNodes = {};
labels = {};
for i = 1:length(netlist)
parts = strsplit(netlist{i});
elements{end+1} = parts{1};
sourceNodes{end+1} = parts{2};
targetNodes{end+1} = parts{3};
labels{end+1} = [parts{4} ‘ – ‘ parts{1}];
end
edgeTable = table(sourceNodes’, targetNodes’, labels’, ‘VariableNames’, {‘EndNodes’, ‘EndNodes2’, ‘Label’});
G = digraph(edgeTable.EndNodes, edgeTable.EndNodes2);
G.Edges.Label = edgeTable.Label;
h = plot(G, ‘EdgeLabel’, G.Edges.Label, ‘NodeLabel’, G.Nodes.Name, ‘Layout’, ‘force’);Hello,
it should be a ridiculously trivial task, but I have to admit I’ve been stuck on it for a few months. Sadly, I’m not very good at Python either, so I’m coming here.

Assume that I have some circuit like the one below:
I want to read and parse a netlist such that I create a digraph object, which can later be used for testing subgraphs being a spanning tree and alike graph theoretic features. Prsing a netlist posses no difficulty, but it looks like the digraph function does not care about the order in my input cells and when I plot the graph, it is labeled wrongly.
I have spent weeks on it with no result. Can you see a easy solution how to turn it into a graph object and plot it accordingly?
Code below produces obvisouly wrong plot, for instance resistors, while the topoogy seems to be idnetified correctly. Edges/Nodes are mislabeled.

clear
close all
clc

netlist = {
‘R1 N001 0 R’;
‘R2 N002 N001 R’;
‘R3 0 N002 R’;
‘C1 N002 N001 C’;
‘C2 N001 0 C’;
‘C3 N002 0 C’;
‘L1 N002 N001 L’;
‘L2 0 N001 L’;
‘L3 0 N002 L’
};
elements = {};
sourceNodes = {};
targetNodes = {};
labels = {};
for i = 1:length(netlist)
parts = strsplit(netlist{i});
elements{end+1} = parts{1};
sourceNodes{end+1} = parts{2};
targetNodes{end+1} = parts{3};
labels{end+1} = [parts{4} ‘ – ‘ parts{1}];
end
edgeTable = table(sourceNodes’, targetNodes’, labels’, ‘VariableNames’, {‘EndNodes’, ‘EndNodes2’, ‘Label’});
G = digraph(edgeTable.EndNodes, edgeTable.EndNodes2);
G.Edges.Label = edgeTable.Label;
h = plot(G, ‘EdgeLabel’, G.Edges.Label, ‘NodeLabel’, G.Nodes.Name, ‘Layout’, ‘force’); Hello,
it should be a ridiculously trivial task, but I have to admit I’ve been stuck on it for a few months. Sadly, I’m not very good at Python either, so I’m coming here.

Assume that I have some circuit like the one below:
I want to read and parse a netlist such that I create a digraph object, which can later be used for testing subgraphs being a spanning tree and alike graph theoretic features. Prsing a netlist posses no difficulty, but it looks like the digraph function does not care about the order in my input cells and when I plot the graph, it is labeled wrongly.
I have spent weeks on it with no result. Can you see a easy solution how to turn it into a graph object and plot it accordingly?
Code below produces obvisouly wrong plot, for instance resistors, while the topoogy seems to be idnetified correctly. Edges/Nodes are mislabeled.

clear
close all
clc

netlist = {
‘R1 N001 0 R’;
‘R2 N002 N001 R’;
‘R3 0 N002 R’;
‘C1 N002 N001 C’;
‘C2 N001 0 C’;
‘C3 N002 0 C’;
‘L1 N002 N001 L’;
‘L2 0 N001 L’;
‘L3 0 N002 L’
};
elements = {};
sourceNodes = {};
targetNodes = {};
labels = {};
for i = 1:length(netlist)
parts = strsplit(netlist{i});
elements{end+1} = parts{1};
sourceNodes{end+1} = parts{2};
targetNodes{end+1} = parts{3};
labels{end+1} = [parts{4} ‘ – ‘ parts{1}];
end
edgeTable = table(sourceNodes’, targetNodes’, labels’, ‘VariableNames’, {‘EndNodes’, ‘EndNodes2’, ‘Label’});
G = digraph(edgeTable.EndNodes, edgeTable.EndNodes2);
G.Edges.Label = edgeTable.Label;
h = plot(G, ‘EdgeLabel’, G.Edges.Label, ‘NodeLabel’, G.Nodes.Name, ‘Layout’, ‘force’); digraph, circuit, netlist, spanning tree, graph plotting, spice MATLAB Answers — New Questions

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