Dear Matlab Community,
I am currently working on a classification task with ECG recordings stored in a CSV file with dimensions of (5001, 12). The first row contains headers, and each column represents a lead of the ECG, totaling 12 leads. These recordings were made over a duration of 10 seconds at a sampling frequency of 500 Hz. Therefore, each lead comprises a sequence of 5000 values. The unit of measurement is 0.01 mV, adhering to the Philips standard recording system.
My specific question pertains to the methodology of feature extraction for classification purposes using the Continuous Wavelet Transform (CWT). Should I proceed by creating a scalogram for each lead independently and then concatenating them, or should I generate 12 scalograms from each ECG and consider them as belonging to the same class?
Put differently, is each lead’s scalogram regarded as a class instance, or is it pivotal to concatenate the 12 leads to accurately represent an ECG? Your insights and guidance on this matter would be greatly appreciated.
Sincerely,Dear Matlab Community,
I am currently working on a classification task with ECG recordings stored in a CSV file with dimensions of (5001, 12). The first row contains headers, and each column represents a lead of the ECG, totaling 12 leads. These recordings were made over a duration of 10 seconds at a sampling frequency of 500 Hz. Therefore, each lead comprises a sequence of 5000 values. The unit of measurement is 0.01 mV, adhering to the Philips standard recording system.
My specific question pertains to the methodology of feature extraction for classification purposes using the Continuous Wavelet Transform (CWT). Should I proceed by creating a scalogram for each lead independently and then concatenating them, or should I generate 12 scalograms from each ECG and consider them as belonging to the same class?
Put differently, is each lead’s scalogram regarded as a class instance, or is it pivotal to concatenate the 12 leads to accurately represent an ECG? Your insights and guidance on this matter would be greatly appreciated.
Sincerely, Dear Matlab Community,
I am currently working on a classification task with ECG recordings stored in a CSV file with dimensions of (5001, 12). The first row contains headers, and each column represents a lead of the ECG, totaling 12 leads. These recordings were made over a duration of 10 seconds at a sampling frequency of 500 Hz. Therefore, each lead comprises a sequence of 5000 values. The unit of measurement is 0.01 mV, adhering to the Philips standard recording system.
My specific question pertains to the methodology of feature extraction for classification purposes using the Continuous Wavelet Transform (CWT). Should I proceed by creating a scalogram for each lead independently and then concatenating them, or should I generate 12 scalograms from each ECG and consider them as belonging to the same class?
Put differently, is each lead’s scalogram regarded as a class instance, or is it pivotal to concatenate the 12 leads to accurately represent an ECG? Your insights and guidance on this matter would be greatly appreciated.
Sincerely, ecg, classification, feature extraction, continuous wavelet transform (cwt), scalogram, lead concatenation, signal processing MATLAB Answers — New Questions

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Does Windows not see Matlab when checking to see if it can do an auto-restart to install updates? I read somewhere that Windows makes sure the computer is not busy before automatically restarting, but alas I came in this morning to find my overnight simulation had been aborted due to Windows installing updates!

:'(Does Windows not see Matlab when checking to see if it can do an auto-restart to install updates? I read somewhere that Windows makes sure the computer is not busy before automatically restarting, but alas I came in this morning to find my overnight simulation had been aborted due to Windows installing updates!

:'( Does Windows not see Matlab when checking to see if it can do an auto-restart to install updates? I read somewhere that Windows makes sure the computer is not busy before automatically restarting, but alas I came in this morning to find my overnight simulation had been aborted due to Windows installing updates!

:'( windows sucks! MATLAB Answers — New Questions

​

Hello,
I am trying to generate some valid pick up configurations for a given orientation ( I have 17 different orientations to test) around some points in space with UR5e. I count them as well so that I know the exact number of valid pick up configurations around each point. The thing is, sometimes when I run the simulation for some points, it counts less or more then it did before. So let’s say the result of the first execution for the point A was 16 valid pick up configs around that point, the second time it can be 13 for example instead of 16. Why is this happening ? Should I modify the solver parameters of gik to overcome this ? Because if sometimes it looks like the UR5 can’t reach certain poses but other times it can so it’s not really viable.

Hello,
I am trying to generate valid pick-up configurations for a given orientation with a UR5e robot. I have 17 different orientations to test around specific points in space. For each point, I count the number of valid pick-up configurations to determine the exact number of feasible configurations.
However, I have encountered an inconsistency: the number of valid configurations varies between simulation runs. For instance, for a given point A, the first execution might yield 16 valid configurations, while the second might yield only 13.
Why is this inconsistency happening? Should I modify the solver parameters of the Generalized Inverse Kinematics (GIK) solver to achieve consistent results ? How can I be sure of getting the maximum reachable configurations? Because of this problem, it appears that sometimes the UR5e can reach certain poses, but other times it cannot, which undermines the reliability of the simulations.
Thank you for your assistance.Hello,
I am trying to generate some valid pick up configurations for a given orientation ( I have 17 different orientations to test) around some points in space with UR5e. I count them as well so that I know the exact number of valid pick up configurations around each point. The thing is, sometimes when I run the simulation for some points, it counts less or more then it did before. So let’s say the result of the first execution for the point A was 16 valid pick up configs around that point, the second time it can be 13 for example instead of 16. Why is this happening ? Should I modify the solver parameters of gik to overcome this ? Because if sometimes it looks like the UR5 can’t reach certain poses but other times it can so it’s not really viable.

Hello,
I am trying to generate valid pick-up configurations for a given orientation with a UR5e robot. I have 17 different orientations to test around specific points in space. For each point, I count the number of valid pick-up configurations to determine the exact number of feasible configurations.
However, I have encountered an inconsistency: the number of valid configurations varies between simulation runs. For instance, for a given point A, the first execution might yield 16 valid configurations, while the second might yield only 13.
Why is this inconsistency happening? Should I modify the solver parameters of the Generalized Inverse Kinematics (GIK) solver to achieve consistent results ? How can I be sure of getting the maximum reachable configurations? Because of this problem, it appears that sometimes the UR5e can reach certain poses, but other times it cannot, which undermines the reliability of the simulations.
Thank you for your assistance. Hello,
I am trying to generate some valid pick up configurations for a given orientation ( I have 17 different orientations to test) around some points in space with UR5e. I count them as well so that I know the exact number of valid pick up configurations around each point. The thing is, sometimes when I run the simulation for some points, it counts less or more then it did before. So let’s say the result of the first execution for the point A was 16 valid pick up configs around that point, the second time it can be 13 for example instead of 16. Why is this happening ? Should I modify the solver parameters of gik to overcome this ? Because if sometimes it looks like the UR5 can’t reach certain poses but other times it can so it’s not really viable.

Hello,
I am trying to generate valid pick-up configurations for a given orientation with a UR5e robot. I have 17 different orientations to test around specific points in space. For each point, I count the number of valid pick-up configurations to determine the exact number of feasible configurations.
However, I have encountered an inconsistency: the number of valid configurations varies between simulation runs. For instance, for a given point A, the first execution might yield 16 valid configurations, while the second might yield only 13.
Why is this inconsistency happening? Should I modify the solver parameters of the Generalized Inverse Kinematics (GIK) solver to achieve consistent results ? How can I be sure of getting the maximum reachable configurations? Because of this problem, it appears that sometimes the UR5e can reach certain poses, but other times it cannot, which undermines the reliability of the simulations.
Thank you for your assistance. matlab, inversekinematics MATLAB Answers — New Questions

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I have a regular for loop which calls a function 44 times. Each iteration takes about 32 seconds to execute in this ordinary for loop. If I use a parfor loop instead, each iteration takes about 8 seconds. However, the final 2 iterations take 10 times as long, 75 and 82 seconds. For example, iteration 22 took 15 seconds in an ordinary for loop, and 75 seconds in the parfor loop, because it happened to be executed as the last iteration. This problem only occurs with the final couple of iterations, never in the middle of the rest of the computation. It occurs with differing datasets. Again, those same iterations , if run in a regular for loop, do not take an inordinate amount of time.
I have preallocated the variables, and don’t use global variables.
What is going on? How can I avoid this?I have a regular for loop which calls a function 44 times. Each iteration takes about 32 seconds to execute in this ordinary for loop. If I use a parfor loop instead, each iteration takes about 8 seconds. However, the final 2 iterations take 10 times as long, 75 and 82 seconds. For example, iteration 22 took 15 seconds in an ordinary for loop, and 75 seconds in the parfor loop, because it happened to be executed as the last iteration. This problem only occurs with the final couple of iterations, never in the middle of the rest of the computation. It occurs with differing datasets. Again, those same iterations , if run in a regular for loop, do not take an inordinate amount of time.
I have preallocated the variables, and don’t use global variables.
What is going on? How can I avoid this? I have a regular for loop which calls a function 44 times. Each iteration takes about 32 seconds to execute in this ordinary for loop. If I use a parfor loop instead, each iteration takes about 8 seconds. However, the final 2 iterations take 10 times as long, 75 and 82 seconds. For example, iteration 22 took 15 seconds in an ordinary for loop, and 75 seconds in the parfor loop, because it happened to be executed as the last iteration. This problem only occurs with the final couple of iterations, never in the middle of the rest of the computation. It occurs with differing datasets. Again, those same iterations , if run in a regular for loop, do not take an inordinate amount of time.
I have preallocated the variables, and don’t use global variables.
What is going on? How can I avoid this? parfor, slow MATLAB Answers — New Questions

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I use Design Expert to generate regression equations from raw input data. These regression equations are integrated into MATLAB to perform genetic algorithm single and multi-objective optimisation using the MATLAB optimisation tool. I’ve been looking to utilise ANFIS using the MATLAB fuzzy logic designer to fine tune the raw data, so that the fine tuned input data and output data can be used for the optimisation instead.
However, the exports I get from the ANFIS simulation results are not (in my opinion) very clear regarding how I can use these fine-tuned datasets subsequent to training the model and obtaining the Root mean square errors. How can one determine the fine tuned input and output data after the training is complete? I see that the training model generates ranges for each of my membership functions per input but from that range, it is difficult to ascertain whether it can give me a specific dataset of values for input and output compared to the raw input and output data that was entered.

The steps I have performed thus far:
I used 85% (26) of the total data set (30) to train the Sugeno model
I used the remaining 15% (4) of the total data set to validate, from which I got tuned predictions of the 4 reference outputs.
From my observation, System Validation takes the trained model and gives ‘tuned’ predicted values of any dataset that is used for validation
In this regard, it is my assumption that by inputting the full 30 reference data sets would give me tuned versions of themselves as tuned output values, which I can re-inject into Design Expert to obtain a ‘tuned’ regression equation, to be used for MATLAB’s genetic algorithm optimisation.
After trying this, I did get higher R squared values for the regression equation as well, which "kind" of confirmed that this works. However, I have not seen this be done anywhere, nor do I know if my interpretation of how to use the system validation via the fuzzy logic designer to get these tuned outputs is correct.

I’ve also attached a code script that was formulated without the use of the Fuzzy Logic Designer tool to try and ascertain the problem. The ‘idea’ centres around re-integrating the 30 datasets into the validation step this time (even though 26 of them were used to train in the first place), but for the purpose of obtaining tuned versions of themselves before using the tuned versions in Design Expert for generating a ‘tuned’ regression equation for MATLAB’s GA Optimisation. Does this make sense?
I am unsure if these are the right steps. Any advice would be greatly appreciated.

% Load your data
load yourData.mat % Replace with your data file

% Combine inputs and outputs into one dataset
data = [inputs, outputs];

% Split data into training and validation sets
numDataPoints = size(data, 1);
trainRatio = 0.85; % 15% for training
numTrain = round(trainRatio * numDataPoints);
trainData = data(1:numTrain, :);
valData = data(numTrain+1:end, :);

% Generate initial FIS using genfis1
numMFs = 4; % Example number of membership functions
fis = genfis1(trainData, numMFs);

% Train the ANFIS model with validation data
[anfisModel, trainError, stepSize, valError] = anfis(trainData, fis, [], [], valData);

% Display the final model structure
disp(anfisModel);

% Assume ‘newInputs’ is the data for which you want to predict the outputs
newInputs = inputs; % Replace with your actual new input data

% Evaluate the model to get the fine-tuned outputs
fineTunedOutputs = evalfis(newInputs, anfisModel);

% Display the fine-tuned outputs
disp(fineTunedOutputs);I use Design Expert to generate regression equations from raw input data. These regression equations are integrated into MATLAB to perform genetic algorithm single and multi-objective optimisation using the MATLAB optimisation tool. I’ve been looking to utilise ANFIS using the MATLAB fuzzy logic designer to fine tune the raw data, so that the fine tuned input data and output data can be used for the optimisation instead.
However, the exports I get from the ANFIS simulation results are not (in my opinion) very clear regarding how I can use these fine-tuned datasets subsequent to training the model and obtaining the Root mean square errors. How can one determine the fine tuned input and output data after the training is complete? I see that the training model generates ranges for each of my membership functions per input but from that range, it is difficult to ascertain whether it can give me a specific dataset of values for input and output compared to the raw input and output data that was entered.

The steps I have performed thus far:
I used 85% (26) of the total data set (30) to train the Sugeno model
I used the remaining 15% (4) of the total data set to validate, from which I got tuned predictions of the 4 reference outputs.
From my observation, System Validation takes the trained model and gives ‘tuned’ predicted values of any dataset that is used for validation
In this regard, it is my assumption that by inputting the full 30 reference data sets would give me tuned versions of themselves as tuned output values, which I can re-inject into Design Expert to obtain a ‘tuned’ regression equation, to be used for MATLAB’s genetic algorithm optimisation.
After trying this, I did get higher R squared values for the regression equation as well, which "kind" of confirmed that this works. However, I have not seen this be done anywhere, nor do I know if my interpretation of how to use the system validation via the fuzzy logic designer to get these tuned outputs is correct.

I’ve also attached a code script that was formulated without the use of the Fuzzy Logic Designer tool to try and ascertain the problem. The ‘idea’ centres around re-integrating the 30 datasets into the validation step this time (even though 26 of them were used to train in the first place), but for the purpose of obtaining tuned versions of themselves before using the tuned versions in Design Expert for generating a ‘tuned’ regression equation for MATLAB’s GA Optimisation. Does this make sense?
I am unsure if these are the right steps. Any advice would be greatly appreciated.

% Load your data
load yourData.mat % Replace with your data file

% Combine inputs and outputs into one dataset
data = [inputs, outputs];

% Split data into training and validation sets
numDataPoints = size(data, 1);
trainRatio = 0.85; % 15% for training
numTrain = round(trainRatio * numDataPoints);
trainData = data(1:numTrain, :);
valData = data(numTrain+1:end, :);

% Generate initial FIS using genfis1
numMFs = 4; % Example number of membership functions
fis = genfis1(trainData, numMFs);

% Train the ANFIS model with validation data
[anfisModel, trainError, stepSize, valError] = anfis(trainData, fis, [], [], valData);

% Display the final model structure
disp(anfisModel);

% Assume ‘newInputs’ is the data for which you want to predict the outputs
newInputs = inputs; % Replace with your actual new input data

% Evaluate the model to get the fine-tuned outputs
fineTunedOutputs = evalfis(newInputs, anfisModel);

% Display the fine-tuned outputs
disp(fineTunedOutputs); I use Design Expert to generate regression equations from raw input data. These regression equations are integrated into MATLAB to perform genetic algorithm single and multi-objective optimisation using the MATLAB optimisation tool. I’ve been looking to utilise ANFIS using the MATLAB fuzzy logic designer to fine tune the raw data, so that the fine tuned input data and output data can be used for the optimisation instead.
However, the exports I get from the ANFIS simulation results are not (in my opinion) very clear regarding how I can use these fine-tuned datasets subsequent to training the model and obtaining the Root mean square errors. How can one determine the fine tuned input and output data after the training is complete? I see that the training model generates ranges for each of my membership functions per input but from that range, it is difficult to ascertain whether it can give me a specific dataset of values for input and output compared to the raw input and output data that was entered.

The steps I have performed thus far:
I used 85% (26) of the total data set (30) to train the Sugeno model
I used the remaining 15% (4) of the total data set to validate, from which I got tuned predictions of the 4 reference outputs.
From my observation, System Validation takes the trained model and gives ‘tuned’ predicted values of any dataset that is used for validation
In this regard, it is my assumption that by inputting the full 30 reference data sets would give me tuned versions of themselves as tuned output values, which I can re-inject into Design Expert to obtain a ‘tuned’ regression equation, to be used for MATLAB’s genetic algorithm optimisation.
After trying this, I did get higher R squared values for the regression equation as well, which "kind" of confirmed that this works. However, I have not seen this be done anywhere, nor do I know if my interpretation of how to use the system validation via the fuzzy logic designer to get these tuned outputs is correct.

I’ve also attached a code script that was formulated without the use of the Fuzzy Logic Designer tool to try and ascertain the problem. The ‘idea’ centres around re-integrating the 30 datasets into the validation step this time (even though 26 of them were used to train in the first place), but for the purpose of obtaining tuned versions of themselves before using the tuned versions in Design Expert for generating a ‘tuned’ regression equation for MATLAB’s GA Optimisation. Does this make sense?
I am unsure if these are the right steps. Any advice would be greatly appreciated.

% Load your data
load yourData.mat % Replace with your data file

% Combine inputs and outputs into one dataset
data = [inputs, outputs];

% Split data into training and validation sets
numDataPoints = size(data, 1);
trainRatio = 0.85; % 15% for training
numTrain = round(trainRatio * numDataPoints);
trainData = data(1:numTrain, :);
valData = data(numTrain+1:end, :);

% Generate initial FIS using genfis1
numMFs = 4; % Example number of membership functions
fis = genfis1(trainData, numMFs);

% Train the ANFIS model with validation data
[anfisModel, trainError, stepSize, valError] = anfis(trainData, fis, [], [], valData);

% Display the final model structure
disp(anfisModel);

% Assume ‘newInputs’ is the data for which you want to predict the outputs
newInputs = inputs; % Replace with your actual new input data

% Evaluate the model to get the fine-tuned outputs
fineTunedOutputs = evalfis(newInputs, anfisModel);

% Display the fine-tuned outputs
disp(fineTunedOutputs); adaptive neuro fuzzy inference system, anfis, single objective optimisation, multi-objective optimisation, genetic algorithm, fuzzy logic designer, design expert, regression equation, objective function MATLAB Answers — New Questions

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I’m attempting to generate Magnetometer measurements for a CubeSat in Simulink using the WMM2020 (World Magnetic Model) Block but when I run a simulation with the block connected the simulation drastically slows down; from 500s instaneously to sticking at 1e-07.
Has anyone used the WMM2020 block and encountered similar problems? If so what did you do to speed up the simulation?I’m attempting to generate Magnetometer measurements for a CubeSat in Simulink using the WMM2020 (World Magnetic Model) Block but when I run a simulation with the block connected the simulation drastically slows down; from 500s instaneously to sticking at 1e-07.
Has anyone used the WMM2020 block and encountered similar problems? If so what did you do to speed up the simulation? I’m attempting to generate Magnetometer measurements for a CubeSat in Simulink using the WMM2020 (World Magnetic Model) Block but when I run a simulation with the block connected the simulation drastically slows down; from 500s instaneously to sticking at 1e-07.
Has anyone used the WMM2020 block and encountered similar problems? If so what did you do to speed up the simulation? wmm2020, simulink, cubesat MATLAB Answers — New Questions

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Hi,
Computing a numerical integration with the Midpoint Method I’m struggling with the output of my function, while with Euler I got the expected result, with this method I’m getting a different graphical solution far away than the analytical solution (and the euler’s one). What could be wrongly specified?
Thanks for your help.
% Midpoint method
% General information
L = 0.61; % [m]
G = 9.81; % gamma [m/s^2]

% Sampling interval
h = 0.1;
%h = 0.05;
%h = 0.01;

ti = 0; % Initial time
tf = 4; % Final time
t = ti:h:tf; % Time vector

function [md] = midpoint(t,L,G,h)
% Initial Value problem
md = zeros(2, length(t));
md(1,1) = pi/40;

for i = 1:length(t)-1
pmd = md(2,i) + (h/2) + (-G/L)*sin(md(1,i)) + (h/2) * [md(2,i); (-G/L)*sin(md(1,i))];
md(:,i+1) = md(:,i) + h * pmd;
end
endHi,
Computing a numerical integration with the Midpoint Method I’m struggling with the output of my function, while with Euler I got the expected result, with this method I’m getting a different graphical solution far away than the analytical solution (and the euler’s one). What could be wrongly specified?
Thanks for your help.
% Midpoint method
% General information
L = 0.61; % [m]
G = 9.81; % gamma [m/s^2]

% Sampling interval
h = 0.1;
%h = 0.05;
%h = 0.01;

ti = 0; % Initial time
tf = 4; % Final time
t = ti:h:tf; % Time vector

function [md] = midpoint(t,L,G,h)
% Initial Value problem
md = zeros(2, length(t));
md(1,1) = pi/40;

for i = 1:length(t)-1
pmd = md(2,i) + (h/2) + (-G/L)*sin(md(1,i)) + (h/2) * [md(2,i); (-G/L)*sin(md(1,i))];
md(:,i+1) = md(:,i) + h * pmd;
end
end Hi,
Computing a numerical integration with the Midpoint Method I’m struggling with the output of my function, while with Euler I got the expected result, with this method I’m getting a different graphical solution far away than the analytical solution (and the euler’s one). What could be wrongly specified?
Thanks for your help.
% Midpoint method
% General information
L = 0.61; % [m]
G = 9.81; % gamma [m/s^2]

% Sampling interval
h = 0.1;
%h = 0.05;
%h = 0.01;

ti = 0; % Initial time
tf = 4; % Final time
t = ti:h:tf; % Time vector

function [md] = midpoint(t,L,G,h)
% Initial Value problem
md = zeros(2, length(t));
md(1,1) = pi/40;

for i = 1:length(t)-1
pmd = md(2,i) + (h/2) + (-G/L)*sin(md(1,i)) + (h/2) * [md(2,i); (-G/L)*sin(md(1,i))];
md(:,i+1) = md(:,i) + h * pmd;
end
end midpoint, numerical integration, for loop, function, indexing, iteration MATLAB Answers — New Questions

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In the model’s parameter script it has various properties for the regenerator including reynold’s number. i know how reynold’s number is caluclated but i cant understand how the assumingly conductive shape factor is calculated and why is it put into Shape factor for laminar flow viscous friction tab in pipe block
regenerator.geometry.length = geometry.displacer_piston.length – 2*crank_wheel.slidercrank_disp.crank_radius;
regenerator.geometry.area = A;
regenerator.geometry.hydraulic_diam = 4*A/P;
regenerator.fric_therm.length_add = 0.01*regenerator.geometry.length;
regenerator.fric_therm.roughness = 15e-6; %[m]
regenerator.fric_therm.Re_lam = 2000;
regenerator.fric_therm.Re_tur = 4000;
regenerator.fric_therm.shape_factor = 64;
regenerator.fric_therm.Nu_lam = 3.66;In the model’s parameter script it has various properties for the regenerator including reynold’s number. i know how reynold’s number is caluclated but i cant understand how the assumingly conductive shape factor is calculated and why is it put into Shape factor for laminar flow viscous friction tab in pipe block
regenerator.geometry.length = geometry.displacer_piston.length – 2*crank_wheel.slidercrank_disp.crank_radius;
regenerator.geometry.area = A;
regenerator.geometry.hydraulic_diam = 4*A/P;
regenerator.fric_therm.length_add = 0.01*regenerator.geometry.length;
regenerator.fric_therm.roughness = 15e-6; %[m]
regenerator.fric_therm.Re_lam = 2000;
regenerator.fric_therm.Re_tur = 4000;
regenerator.fric_therm.shape_factor = 64;
regenerator.fric_therm.Nu_lam = 3.66; In the model’s parameter script it has various properties for the regenerator including reynold’s number. i know how reynold’s number is caluclated but i cant understand how the assumingly conductive shape factor is calculated and why is it put into Shape factor for laminar flow viscous friction tab in pipe block
regenerator.geometry.length = geometry.displacer_piston.length – 2*crank_wheel.slidercrank_disp.crank_radius;
regenerator.geometry.area = A;
regenerator.geometry.hydraulic_diam = 4*A/P;
regenerator.fric_therm.length_add = 0.01*regenerator.geometry.length;
regenerator.fric_therm.roughness = 15e-6; %[m]
regenerator.fric_therm.Re_lam = 2000;
regenerator.fric_therm.Re_tur = 4000;
regenerator.fric_therm.shape_factor = 64;
regenerator.fric_therm.Nu_lam = 3.66; simscape MATLAB Answers — New Questions

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clc;
clear;
THT = 0:12:360;
L_spm = [67.14387343 67.08087868 71.27139857 77.8011002 83.48598354 86.53313448 86.96218524 86.54264509 86.55222711 86.96002063 86.43128473 83.48041917 77.77271123 71.24225453 67.07111986 67.25745156 67.08157624 71.23009025 77.76475253 83.46615144 86.42639244 86.96777069 86.54247287 86.56340306 86.9412182 86.52945688 83.49766473 77.78259172 71.25635182 67.06788196 67.14602485];
How to differentiate L_spm with respect to THT? Thanks.clc;
clear;
THT = 0:12:360;
L_spm = [67.14387343 67.08087868 71.27139857 77.8011002 83.48598354 86.53313448 86.96218524 86.54264509 86.55222711 86.96002063 86.43128473 83.48041917 77.77271123 71.24225453 67.07111986 67.25745156 67.08157624 71.23009025 77.76475253 83.46615144 86.42639244 86.96777069 86.54247287 86.56340306 86.9412182 86.52945688 83.49766473 77.78259172 71.25635182 67.06788196 67.14602485];
How to differentiate L_spm with respect to THT? Thanks. clc;
clear;
THT = 0:12:360;
L_spm = [67.14387343 67.08087868 71.27139857 77.8011002 83.48598354 86.53313448 86.96218524 86.54264509 86.55222711 86.96002063 86.43128473 83.48041917 77.77271123 71.24225453 67.07111986 67.25745156 67.08157624 71.23009025 77.76475253 83.46615144 86.42639244 86.96777069 86.54247287 86.56340306 86.9412182 86.52945688 83.49766473 77.78259172 71.25635182 67.06788196 67.14602485];
How to differentiate L_spm with respect to THT? Thanks. differentiation MATLAB Answers — New Questions

​

I have wrote some MATLAB code, using some parameters from genetic algorithm input to EES for calculating thermodynamic properties. Then I get the calculated result and output from EES to matlab. But I met some problems, here is the connecting matlab code below:

Because I have bought the academic version EES software, I input the parameters into EES by writing parameter value in dat file, and EES got the data from dat file. However, the calculating process stopped in the EES interface, no command have executed after that. If the program runs successfully, EES should return calculating results to MATLAB within seconds.
I have no idea what wrong with it, can anyone help me? I can’t thank you enough.I have wrote some MATLAB code, using some parameters from genetic algorithm input to EES for calculating thermodynamic properties. Then I get the calculated result and output from EES to matlab. But I met some problems, here is the connecting matlab code below:

Because I have bought the academic version EES software, I input the parameters into EES by writing parameter value in dat file, and EES got the data from dat file. However, the calculating process stopped in the EES interface, no command have executed after that. If the program runs successfully, EES should return calculating results to MATLAB within seconds.
I have no idea what wrong with it, can anyone help me? I can’t thank you enough. I have wrote some MATLAB code, using some parameters from genetic algorithm input to EES for calculating thermodynamic properties. Then I get the calculated result and output from EES to matlab. But I met some problems, here is the connecting matlab code below:

Because I have bought the academic version EES software, I input the parameters into EES by writing parameter value in dat file, and EES got the data from dat file. However, the calculating process stopped in the EES interface, no command have executed after that. If the program runs successfully, EES should return calculating results to MATLAB within seconds.
I have no idea what wrong with it, can anyone help me? I can’t thank you enough. engineering equation solver, ees, interaction MATLAB Answers — New Questions

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