For a while loop how I can calculate the values at each hour before do a break? As we know while loop only performs the condition statment and breaks the loop, then gives you the value at that specific hour only. I provide te example below :
for i=1:Time
while water_tank_soc(i-1) < Water_tank_capacity_max
m_net_o(i) =………
m_net_i(i) = ……
% Update SOC
water_tank_soc(i) = water_tank_soc(i-1) + m_net_i(i) – m_net_o(i);
if water_tank_soc(i) >= Water_tank_capacity_max
break
end
% Update for next time step
water_tank_soc(i-1) = water_tank_soc(i); % Update previous state for next iteration
end
endFor a while loop how I can calculate the values at each hour before do a break? As we know while loop only performs the condition statment and breaks the loop, then gives you the value at that specific hour only. I provide te example below :
for i=1:Time
while water_tank_soc(i-1) < Water_tank_capacity_max
m_net_o(i) =………
m_net_i(i) = ……
% Update SOC
water_tank_soc(i) = water_tank_soc(i-1) + m_net_i(i) – m_net_o(i);
if water_tank_soc(i) >= Water_tank_capacity_max
break
end
% Update for next time step
water_tank_soc(i-1) = water_tank_soc(i); % Update previous state for next iteration
end
end For a while loop how I can calculate the values at each hour before do a break? As we know while loop only performs the condition statment and breaks the loop, then gives you the value at that specific hour only. I provide te example below :
for i=1:Time
while water_tank_soc(i-1) < Water_tank_capacity_max
m_net_o(i) =………
m_net_i(i) = ……
% Update SOC
water_tank_soc(i) = water_tank_soc(i-1) + m_net_i(i) – m_net_o(i);
if water_tank_soc(i) >= Water_tank_capacity_max
break
end
% Update for next time step
water_tank_soc(i-1) = water_tank_soc(i); % Update previous state for next iteration
end
end save a values, while loop MATLAB Answers — New Questions

​

Hi everyone,
I’m having some trouble understanding Gaussian Process Regression (GPR) options in the Regression Learner App. There are three main choices for GPR models:
Predefined Kernel: I can directly choose a kernel (Rational Quadratic, Squared Exponential, Matern 5/2, or Exponential) if I know which one suits my data best.
All GPR Models (non-optimizable): If I’m unsure which kernel to use, I can select this option to try all non-optimizable GPR models.
Optimizable GPR: This option allows the hyperparameters to be optimized and has even more Kernels available.
Regardless of whether I choose the optimizable or non-optimizable version, each kernel has hyperparameters that can be tuned.
Here are my questions:
Automatic Hyperparameter Selection: For the standard model without optimization, the kernel parameters (hyperparameters) are automatically selected and are an initial best guess rather than the optimal ones to my understanding. If so, how are they estimated/selected, and why aren’t they optimal?
Optimization Process: When the optimization option is selected, the hyperparameters are found by maximizing the Log Marginal Likelihood function. So, they are not guessed, but these are the hyperparameters that best describe the data. So finding the best value is basically the optimization process?
Choosing Non-Optimal Hyperparameters: Why would someone use the version without the best hyperparameters? One reason I’ve encountered is that optimization can take too long with the same data. Are there other reasons to choose non-optimizable models?
Best Practices: Are there standard practices or guidelines for when to use each version of the GPR models? Or one usually starts with non-optimizable version and then go to the optimizable one if the results are not sufficient enough?
Output Differences: Both versions output forecasted data and the covariance matrix. Does the optimized version provide any additional information or benefits?
Plotting Covariance: How can I plot the covariance that quantifies the uncertainty of my predictions? This is one of the main advantages of using GPR, and I want to visualize it. I have found some sample code here https://ch.mathworks.com/help/stats/gaussian-process-regression-models.html But I am a bit confused, because my input is a table actually, so I do not have only one predictor but a table. So far I have always used “predictedData = trainedModel.predictFcn(Table); but this gives me only the forecasted data without the 95% prediction intervals
I’m generally confused about the differences between both versions and the results they produce. Any insights or explanations would be greatly appreciated.
Thanks in advance!
Best regards,
GeorgiHi everyone,
I’m having some trouble understanding Gaussian Process Regression (GPR) options in the Regression Learner App. There are three main choices for GPR models:
Predefined Kernel: I can directly choose a kernel (Rational Quadratic, Squared Exponential, Matern 5/2, or Exponential) if I know which one suits my data best.
All GPR Models (non-optimizable): If I’m unsure which kernel to use, I can select this option to try all non-optimizable GPR models.
Optimizable GPR: This option allows the hyperparameters to be optimized and has even more Kernels available.
Regardless of whether I choose the optimizable or non-optimizable version, each kernel has hyperparameters that can be tuned.
Here are my questions:
Automatic Hyperparameter Selection: For the standard model without optimization, the kernel parameters (hyperparameters) are automatically selected and are an initial best guess rather than the optimal ones to my understanding. If so, how are they estimated/selected, and why aren’t they optimal?
Optimization Process: When the optimization option is selected, the hyperparameters are found by maximizing the Log Marginal Likelihood function. So, they are not guessed, but these are the hyperparameters that best describe the data. So finding the best value is basically the optimization process?
Choosing Non-Optimal Hyperparameters: Why would someone use the version without the best hyperparameters? One reason I’ve encountered is that optimization can take too long with the same data. Are there other reasons to choose non-optimizable models?
Best Practices: Are there standard practices or guidelines for when to use each version of the GPR models? Or one usually starts with non-optimizable version and then go to the optimizable one if the results are not sufficient enough?
Output Differences: Both versions output forecasted data and the covariance matrix. Does the optimized version provide any additional information or benefits?
Plotting Covariance: How can I plot the covariance that quantifies the uncertainty of my predictions? This is one of the main advantages of using GPR, and I want to visualize it. I have found some sample code here https://ch.mathworks.com/help/stats/gaussian-process-regression-models.html But I am a bit confused, because my input is a table actually, so I do not have only one predictor but a table. So far I have always used “predictedData = trainedModel.predictFcn(Table); but this gives me only the forecasted data without the 95% prediction intervals
I’m generally confused about the differences between both versions and the results they produce. Any insights or explanations would be greatly appreciated.
Thanks in advance!
Best regards,
Georgi Hi everyone,
I’m having some trouble understanding Gaussian Process Regression (GPR) options in the Regression Learner App. There are three main choices for GPR models:
Predefined Kernel: I can directly choose a kernel (Rational Quadratic, Squared Exponential, Matern 5/2, or Exponential) if I know which one suits my data best.
All GPR Models (non-optimizable): If I’m unsure which kernel to use, I can select this option to try all non-optimizable GPR models.
Optimizable GPR: This option allows the hyperparameters to be optimized and has even more Kernels available.
Regardless of whether I choose the optimizable or non-optimizable version, each kernel has hyperparameters that can be tuned.
Here are my questions:
Automatic Hyperparameter Selection: For the standard model without optimization, the kernel parameters (hyperparameters) are automatically selected and are an initial best guess rather than the optimal ones to my understanding. If so, how are they estimated/selected, and why aren’t they optimal?
Optimization Process: When the optimization option is selected, the hyperparameters are found by maximizing the Log Marginal Likelihood function. So, they are not guessed, but these are the hyperparameters that best describe the data. So finding the best value is basically the optimization process?
Choosing Non-Optimal Hyperparameters: Why would someone use the version without the best hyperparameters? One reason I’ve encountered is that optimization can take too long with the same data. Are there other reasons to choose non-optimizable models?
Best Practices: Are there standard practices or guidelines for when to use each version of the GPR models? Or one usually starts with non-optimizable version and then go to the optimizable one if the results are not sufficient enough?
Output Differences: Both versions output forecasted data and the covariance matrix. Does the optimized version provide any additional information or benefits?
Plotting Covariance: How can I plot the covariance that quantifies the uncertainty of my predictions? This is one of the main advantages of using GPR, and I want to visualize it. I have found some sample code here https://ch.mathworks.com/help/stats/gaussian-process-regression-models.html But I am a bit confused, because my input is a table actually, so I do not have only one predictor but a table. So far I have always used “predictedData = trainedModel.predictFcn(Table); but this gives me only the forecasted data without the 95% prediction intervals
I’m generally confused about the differences between both versions and the results they produce. Any insights or explanations would be greatly appreciated.
Thanks in advance!
Best regards,
Georgi regression, optimization MATLAB Answers — New Questions

​

Can I solve sum of polynomial square in a one variable ?
e.g. (1+x)^2 + (3+x)^2 +2*x =
Can Mathlab compute result as x variable ?Can I solve sum of polynomial square in a one variable ?
e.g. (1+x)^2 + (3+x)^2 +2*x =
Can Mathlab compute result as x variable ? Can I solve sum of polynomial square in a one variable ?
e.g. (1+x)^2 + (3+x)^2 +2*x =
Can Mathlab compute result as x variable ? polynomial square MATLAB Answers — New Questions

​

Hello. I’m a newbie to stateflow in Simulink. Around 2-3 days ago, I figured out someting making me confused a little bit. I set the sample time in Model Configuration Paremeters to 0.01 sec, as shown in Fig 1. Fig 2. to Fig 3. shows the working process at each time step of the system wroted by myself, which consists of 3 parellel parent states. All of them are related to time counters (timer1, timer2 and timer3, which are local variables).

Fig 1. Sample time setting in Configuration Parameters (0.01 sec)

Fig 2. T = 0.000 sec

Fig 3. T = 0.010 sec

Fig 4. T = 0.020 sec

Fig 5. T = 0.030 sec

Fig 6. T = 0.040 sec
*Note that T is executing time, shown at the lowest of each picture.
According to the Fig 5., when T = 0.030 sec and all values of timer counters (timer1, timer2 and timer3) already reached to "3", there were only the buttom parent state, "Sys3_condition_action", that moved itself from "Off" state to "On" state, although all transitions between "On" and "Off" were satisfied with their timer counts (equal to 3). While the upper and middle parent states, Sys1_during and Sys2_cyclic_updating respectively, would move to "On" state in the next time step, at T = 0.040 sec (shown by Fig 6.).
At the first, I thought that all parents state should work in the same manner (all transitions should occur at T = 0.030 sec). But in fact, it’s not what I understand. Could someone explain to me why they worked differently?
Best Regards.Hello. I’m a newbie to stateflow in Simulink. Around 2-3 days ago, I figured out someting making me confused a little bit. I set the sample time in Model Configuration Paremeters to 0.01 sec, as shown in Fig 1. Fig 2. to Fig 3. shows the working process at each time step of the system wroted by myself, which consists of 3 parellel parent states. All of them are related to time counters (timer1, timer2 and timer3, which are local variables).

Fig 1. Sample time setting in Configuration Parameters (0.01 sec)

Fig 2. T = 0.000 sec

Fig 3. T = 0.010 sec

Fig 4. T = 0.020 sec

Fig 5. T = 0.030 sec

Fig 6. T = 0.040 sec
*Note that T is executing time, shown at the lowest of each picture.
According to the Fig 5., when T = 0.030 sec and all values of timer counters (timer1, timer2 and timer3) already reached to "3", there were only the buttom parent state, "Sys3_condition_action", that moved itself from "Off" state to "On" state, although all transitions between "On" and "Off" were satisfied with their timer counts (equal to 3). While the upper and middle parent states, Sys1_during and Sys2_cyclic_updating respectively, would move to "On" state in the next time step, at T = 0.040 sec (shown by Fig 6.).
At the first, I thought that all parents state should work in the same manner (all transitions should occur at T = 0.030 sec). But in fact, it’s not what I understand. Could someone explain to me why they worked differently?
Best Regards. Hello. I’m a newbie to stateflow in Simulink. Around 2-3 days ago, I figured out someting making me confused a little bit. I set the sample time in Model Configuration Paremeters to 0.01 sec, as shown in Fig 1. Fig 2. to Fig 3. shows the working process at each time step of the system wroted by myself, which consists of 3 parellel parent states. All of them are related to time counters (timer1, timer2 and timer3, which are local variables).

Fig 1. Sample time setting in Configuration Parameters (0.01 sec)

Fig 2. T = 0.000 sec

Fig 3. T = 0.010 sec

Fig 4. T = 0.020 sec

Fig 5. T = 0.030 sec

Fig 6. T = 0.040 sec
*Note that T is executing time, shown at the lowest of each picture.
According to the Fig 5., when T = 0.030 sec and all values of timer counters (timer1, timer2 and timer3) already reached to "3", there were only the buttom parent state, "Sys3_condition_action", that moved itself from "Off" state to "On" state, although all transitions between "On" and "Off" were satisfied with their timer counts (equal to 3). While the upper and middle parent states, Sys1_during and Sys2_cyclic_updating respectively, would move to "On" state in the next time step, at T = 0.040 sec (shown by Fig 6.).
At the first, I thought that all parents state should work in the same manner (all transitions should occur at T = 0.030 sec). But in fact, it’s not what I understand. Could someone explain to me why they worked differently?
Best Regards. stateflow, simulink MATLAB Answers — New Questions

​