Thank you for taking the time to help. I will do my best to be clear and concise.
I have 3 sets of data (atttached), each with x and y data – not necesarilly the same length.
Each y(x) function can be normalized into p(t) using 4 parameters:
A – a constant that is extracted from each individual y(x), ‘A’ looks like [A1, A2, A3]
B – a fitting parameter unique to each y(x)
C – a fitting parameter applied to all y(x)
D – a constraint variable dependent on A and B applied to all y(x)
So the set looks something like:
I would like to use lsqcurvefit to fit my data to my function p(t) and output the unique (C and D) values.
I have a final constraint to increase the accuracy of lsqcurvefit: , where D is another global constant like C.
Where I’m at now, I want to adjust C and D to fit my function p(t) to y(x), but I don’t know how to also include the unique A values for each y(x).
I’ve pasted my current code below where I am able to get an individual fit for each y(x) but without consistent C and D parameters.
%% Data analysis
Results = NaN(3,2); % Result [C,D] for each individual fitting
for t = 1:3
%TL is summary matrix of all y(x) for the 3 experiments, NaN fills in
%where some y(x) are shorter than others.
x = TL(~isnan(TL(:,2*t-1)),2*t-1); %extracts x values from TL
y = TL(~isnan(TL(:,2*t)),2*t); %extracts y values from TL

%P = parameters: P(1) = C | P(2) = D
%fun below is the normalization for p(t)
fun = @(P,x)A(t)*(1-1/(2*(1-P(1))))*(1.304*exp(-(P(2)*x/(r/1000)^2).^0.5)-0.304*exp(-0.254*P(2)*x/(r/1000)^2))+1/(2*(1-P(1)));
P0 = [0.4, 1*10^-9]; %Initial parameters
lb = [0.1, 1*10^-10]; %Lower bound
ub = [0.5, 1*10^-8]; %Upper bound

Results(t,:) = lsqcurvefit(fun,P0,x,y,lb,ub); %Result consolidation for individual fitting

subplot(3,1,t)
plot(x,y,’ko’,x,fun(Results(t,:),x),’b-‘)
end
From looking at other posts on using lsqcurvefit with multiple datasets, I think my obstacle has come down to whether I should (1) run lsqcurvefit on all y(x) and somehow input the unique A constants or (2) run lsqcurvefit on individual y(x) and somehow correlate C and D to match between each fitting.
I hope this is presented clearly, looking forward to your thoughts and discussion – Thanks.Thank you for taking the time to help. I will do my best to be clear and concise.
I have 3 sets of data (atttached), each with x and y data – not necesarilly the same length.
Each y(x) function can be normalized into p(t) using 4 parameters:
A – a constant that is extracted from each individual y(x), ‘A’ looks like [A1, A2, A3]
B – a fitting parameter unique to each y(x)
C – a fitting parameter applied to all y(x)
D – a constraint variable dependent on A and B applied to all y(x)
So the set looks something like:
I would like to use lsqcurvefit to fit my data to my function p(t) and output the unique (C and D) values.
I have a final constraint to increase the accuracy of lsqcurvefit: , where D is another global constant like C.
Where I’m at now, I want to adjust C and D to fit my function p(t) to y(x), but I don’t know how to also include the unique A values for each y(x).
I’ve pasted my current code below where I am able to get an individual fit for each y(x) but without consistent C and D parameters.
%% Data analysis
Results = NaN(3,2); % Result [C,D] for each individual fitting
for t = 1:3
%TL is summary matrix of all y(x) for the 3 experiments, NaN fills in
%where some y(x) are shorter than others.
x = TL(~isnan(TL(:,2*t-1)),2*t-1); %extracts x values from TL
y = TL(~isnan(TL(:,2*t)),2*t); %extracts y values from TL

%P = parameters: P(1) = C | P(2) = D
%fun below is the normalization for p(t)
fun = @(P,x)A(t)*(1-1/(2*(1-P(1))))*(1.304*exp(-(P(2)*x/(r/1000)^2).^0.5)-0.304*exp(-0.254*P(2)*x/(r/1000)^2))+1/(2*(1-P(1)));
P0 = [0.4, 1*10^-9]; %Initial parameters
lb = [0.1, 1*10^-10]; %Lower bound
ub = [0.5, 1*10^-8]; %Upper bound

Results(t,:) = lsqcurvefit(fun,P0,x,y,lb,ub); %Result consolidation for individual fitting

subplot(3,1,t)
plot(x,y,’ko’,x,fun(Results(t,:),x),’b-‘)
end
From looking at other posts on using lsqcurvefit with multiple datasets, I think my obstacle has come down to whether I should (1) run lsqcurvefit on all y(x) and somehow input the unique A constants or (2) run lsqcurvefit on individual y(x) and somehow correlate C and D to match between each fitting.
I hope this is presented clearly, looking forward to your thoughts and discussion – Thanks. Thank you for taking the time to help. I will do my best to be clear and concise.
I have 3 sets of data (atttached), each with x and y data – not necesarilly the same length.
Each y(x) function can be normalized into p(t) using 4 parameters:
A – a constant that is extracted from each individual y(x), ‘A’ looks like [A1, A2, A3]
B – a fitting parameter unique to each y(x)
C – a fitting parameter applied to all y(x)
D – a constraint variable dependent on A and B applied to all y(x)
So the set looks something like:
I would like to use lsqcurvefit to fit my data to my function p(t) and output the unique (C and D) values.
I have a final constraint to increase the accuracy of lsqcurvefit: , where D is another global constant like C.
Where I’m at now, I want to adjust C and D to fit my function p(t) to y(x), but I don’t know how to also include the unique A values for each y(x).
I’ve pasted my current code below where I am able to get an individual fit for each y(x) but without consistent C and D parameters.
%% Data analysis
Results = NaN(3,2); % Result [C,D] for each individual fitting
for t = 1:3
%TL is summary matrix of all y(x) for the 3 experiments, NaN fills in
%where some y(x) are shorter than others.
x = TL(~isnan(TL(:,2*t-1)),2*t-1); %extracts x values from TL
y = TL(~isnan(TL(:,2*t)),2*t); %extracts y values from TL

%P = parameters: P(1) = C | P(2) = D
%fun below is the normalization for p(t)
fun = @(P,x)A(t)*(1-1/(2*(1-P(1))))*(1.304*exp(-(P(2)*x/(r/1000)^2).^0.5)-0.304*exp(-0.254*P(2)*x/(r/1000)^2))+1/(2*(1-P(1)));
P0 = [0.4, 1*10^-9]; %Initial parameters
lb = [0.1, 1*10^-10]; %Lower bound
ub = [0.5, 1*10^-8]; %Upper bound

Results(t,:) = lsqcurvefit(fun,P0,x,y,lb,ub); %Result consolidation for individual fitting

subplot(3,1,t)
plot(x,y,’ko’,x,fun(Results(t,:),x),’b-‘)
end
From looking at other posts on using lsqcurvefit with multiple datasets, I think my obstacle has come down to whether I should (1) run lsqcurvefit on all y(x) and somehow input the unique A constants or (2) run lsqcurvefit on individual y(x) and somehow correlate C and D to match between each fitting.
I hope this is presented clearly, looking forward to your thoughts and discussion – Thanks. lsqcurvefit, curve fitting, optimization MATLAB Answers — New Questions

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I have blast data from 3 different organisms. Two animals and one human set. I am looking to develop a transfer function to translate the data sets from animal set to the human data set. I also have the ambient pressure from outside of the body for normalization as well as weight, speed of the wave, and weight of the individual body parts. I only have acess to the following toolboxes:
MATLAB Version 9.8 (R2020a)
Curve Fitting Toolbox Version 3.5.11 (R2020a)
Deep Learning Toolbox Version 14.0 (R2020a)
GUI Layout Toolbox Version 2.3.6 (R2023a)
Signal Processing Toolbox Version 8.4 (R2020a)
Statistics and Machine Learning Toolbox Version 11.7 (R2020a)

I want to be able to set up some sort of optimizaiton funciton that would work between both of the animal models, independetly. Likely a second order model. I have attached the raw data of the pressures as a CSV. Each of the data sets are computed at 0.8Mhz and the data listed by row is an maximum individual test, not a continous data set.

I am looking for gudiance on how to start this.
>>I have blast data from 3 different organisms. Two animals and one human set. I am looking to develop a transfer function to translate the data sets from animal set to the human data set. I also have the ambient pressure from outside of the body for normalization as well as weight, speed of the wave, and weight of the individual body parts. I only have acess to the following toolboxes:
MATLAB Version 9.8 (R2020a)
Curve Fitting Toolbox Version 3.5.11 (R2020a)
Deep Learning Toolbox Version 14.0 (R2020a)
GUI Layout Toolbox Version 2.3.6 (R2023a)
Signal Processing Toolbox Version 8.4 (R2020a)
Statistics and Machine Learning Toolbox Version 11.7 (R2020a)

I want to be able to set up some sort of optimizaiton funciton that would work between both of the animal models, independetly. Likely a second order model. I have attached the raw data of the pressures as a CSV. Each of the data sets are computed at 0.8Mhz and the data listed by row is an maximum individual test, not a continous data set.

I am looking for gudiance on how to start this.
>> I have blast data from 3 different organisms. Two animals and one human set. I am looking to develop a transfer function to translate the data sets from animal set to the human data set. I also have the ambient pressure from outside of the body for normalization as well as weight, speed of the wave, and weight of the individual body parts. I only have acess to the following toolboxes:
MATLAB Version 9.8 (R2020a)
Curve Fitting Toolbox Version 3.5.11 (R2020a)
Deep Learning Toolbox Version 14.0 (R2020a)
GUI Layout Toolbox Version 2.3.6 (R2023a)
Signal Processing Toolbox Version 8.4 (R2020a)
Statistics and Machine Learning Toolbox Version 11.7 (R2020a)

I want to be able to set up some sort of optimizaiton funciton that would work between both of the animal models, independetly. Likely a second order model. I have attached the raw data of the pressures as a CSV. Each of the data sets are computed at 0.8Mhz and the data listed by row is an maximum individual test, not a continous data set.

I am looking for gudiance on how to start this.
>> transfer function, biologics, curve fitting MATLAB Answers — New Questions

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Hello together,
I used the function »feedback« to generate a closed-loop system, which worked well. Now I am trying to understand, how this function is mathematically implemented in Matlab. There are a lot of information’s, how to use the function, but I didn’t find anything which describes how it’s working and implemented mathematically. Does this function use iterative every sample from the output and add it to the input?
Thank you!Hello together,
I used the function »feedback« to generate a closed-loop system, which worked well. Now I am trying to understand, how this function is mathematically implemented in Matlab. There are a lot of information’s, how to use the function, but I didn’t find anything which describes how it’s working and implemented mathematically. Does this function use iterative every sample from the output and add it to the input?
Thank you! Hello together,
I used the function »feedback« to generate a closed-loop system, which worked well. Now I am trying to understand, how this function is mathematically implemented in Matlab. There are a lot of information’s, how to use the function, but I didn’t find anything which describes how it’s working and implemented mathematically. Does this function use iterative every sample from the output and add it to the input?
Thank you! feedback, closed loop, control MATLAB Answers — New Questions

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I want to use the function equilibrate, but I don’t understand the following written in Rescaling to Solve a Linear System.
"equilibrate is most useful when the scales of the b and x vectors in the original system x = Ab are irrelevant. However, if the scales of b and x are relevant, then using equilibrate to rescale A only for the linear system solve is not recommended. The obtained solution does not generally yield a small residual for the original system, even if expressed using the original basis vectors."
Could someone explain the meaning of this with an example?I want to use the function equilibrate, but I don’t understand the following written in Rescaling to Solve a Linear System.
"equilibrate is most useful when the scales of the b and x vectors in the original system x = Ab are irrelevant. However, if the scales of b and x are relevant, then using equilibrate to rescale A only for the linear system solve is not recommended. The obtained solution does not generally yield a small residual for the original system, even if expressed using the original basis vectors."
Could someone explain the meaning of this with an example? I want to use the function equilibrate, but I don’t understand the following written in Rescaling to Solve a Linear System.
"equilibrate is most useful when the scales of the b and x vectors in the original system x = Ab are irrelevant. However, if the scales of b and x are relevant, then using equilibrate to rescale A only for the linear system solve is not recommended. The obtained solution does not generally yield a small residual for the original system, even if expressed using the original basis vectors."
Could someone explain the meaning of this with an example? matrices MATLAB Answers — New Questions

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want to find the values of ‘K’ for which the function (D) becomes zero. ‘L’ and ‘a’ are the constants. Code:
syms A B L K a
D = [cosh(K*L)-cos(K*L), sinh(K*L)-sin(K*L); sinh(K*L)+sin(K*L)+K*a*L*(cosh(K*L)+cos(K*L)), cosh(K*L)-cos(K*L)+K*a*L*(sinh(K*L)+sin(K*L))];
d = det(D)
fzero(@(K) d, 10)
Error using fzero (line 309) Function value at starting guess must be finite and real.want to find the values of ‘K’ for which the function (D) becomes zero. ‘L’ and ‘a’ are the constants. Code:
syms A B L K a
D = [cosh(K*L)-cos(K*L), sinh(K*L)-sin(K*L); sinh(K*L)+sin(K*L)+K*a*L*(cosh(K*L)+cos(K*L)), cosh(K*L)-cos(K*L)+K*a*L*(sinh(K*L)+sin(K*L))];
d = det(D)
fzero(@(K) d, 10)
Error using fzero (line 309) Function value at starting guess must be finite and real. want to find the values of ‘K’ for which the function (D) becomes zero. ‘L’ and ‘a’ are the constants. Code:
syms A B L K a
D = [cosh(K*L)-cos(K*L), sinh(K*L)-sin(K*L); sinh(K*L)+sin(K*L)+K*a*L*(cosh(K*L)+cos(K*L)), cosh(K*L)-cos(K*L)+K*a*L*(sinh(K*L)+sin(K*L))];
d = det(D)
fzero(@(K) d, 10)
Error using fzero (line 309) Function value at starting guess must be finite and real. roots, non-polynomial MATLAB Answers — New Questions

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Is code generation supported for "convolution1DLayer"?Is code generation supported for "convolution1DLayer"? Is code generation supported for "convolution1DLayer"? code, generation, convolution1dlayer, convolution, 1d, layer, deep, learning, neural, network, matlab, coder, spatial, temporal MATLAB Answers — New Questions

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I have a nested cell array {48×691}. I would like to isolate an entire column of the nested cell array while grabbing all of the rows such that I would get the nested arrays in this way:
{:,1} {:, 2} {:,3} … {:,691}

In other words, how do I get all of the 691 columns from the nested array extracted such that I grab the 48 rows each time?

Thank you!I have a nested cell array {48×691}. I would like to isolate an entire column of the nested cell array while grabbing all of the rows such that I would get the nested arrays in this way:
{:,1} {:, 2} {:,3} … {:,691}

In other words, how do I get all of the 691 columns from the nested array extracted such that I grab the 48 rows each time?

Thank you! I have a nested cell array {48×691}. I would like to isolate an entire column of the nested cell array while grabbing all of the rows such that I would get the nested arrays in this way:
{:,1} {:, 2} {:,3} … {:,691}

In other words, how do I get all of the 691 columns from the nested array extracted such that I grab the 48 rows each time?

Thank you! indexing nested cell array, nested cell array, indexing MATLAB Answers — New Questions

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I am trying to run a Simulink mode via External Mode. Part of my model is a Protected Model Reference and I am using a release before release MATLAB R2023b.
 
If I attempt to run this model in External Mode it fails with the following error message:

Protected models do not work in External mode or rapid accelerator simulations.
Is it possible to protect a model and still be able to run it in External mode on these releases?I am trying to run a Simulink mode via External Mode. Part of my model is a Protected Model Reference and I am using a release before release MATLAB R2023b.
 
If I attempt to run this model in External Mode it fails with the following error message:

Protected models do not work in External mode or rapid accelerator simulations.
Is it possible to protect a model and still be able to run it in External mode on these releases? I am trying to run a Simulink mode via External Mode. Part of my model is a Protected Model Reference and I am using a release before release MATLAB R2023b.
 
If I attempt to run this model in External Mode it fails with the following error message:

Protected models do not work in External mode or rapid accelerator simulations.
Is it possible to protect a model and still be able to run it in External mode on these releases? protected, external, mode MATLAB Answers — New Questions

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Matlab asks me to sign in when I’m offline, as this is impossible I can’t use Matlab while offline.
I can use Matlab no problem when I’m online and it does not ask me to sign in.
When I’m offline a Sign In window appears with this error:
Unable to contact login services. There may be a problem with your internet connection, or the service may be temporarily unavailable. If the problem persists contact MathWorks technical support.
Any fix for this?Matlab asks me to sign in when I’m offline, as this is impossible I can’t use Matlab while offline.
I can use Matlab no problem when I’m online and it does not ask me to sign in.
When I’m offline a Sign In window appears with this error:
Unable to contact login services. There may be a problem with your internet connection, or the service may be temporarily unavailable. If the problem persists contact MathWorks technical support.
Any fix for this? Matlab asks me to sign in when I’m offline, as this is impossible I can’t use Matlab while offline.
I can use Matlab no problem when I’m online and it does not ask me to sign in.
When I’m offline a Sign In window appears with this error:
Unable to contact login services. There may be a problem with your internet connection, or the service may be temporarily unavailable. If the problem persists contact MathWorks technical support.
Any fix for this? login, offline MATLAB Answers — New Questions

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i hv theta value as 60 and 61 each theta value has phi of 120 240 with corresponding total values.
i hv to mirror data that is phi values data from 120 to 180 should be mirrored to 180 to 240 and then its plot is plotted. aim is to get plots symmetry xaxis is phi y axis is total with respect to theta percentile plots.i hv tried with excel sheet but my data is in .tab file how to import that format file and plot from it

a=xlsread(‘1GHzHH.xlsx’);
theta=a(:,1);
phi=a(:,2);
total=a(:,3);
figure
plot(phi,total)
% f=[1];
% pol=[‘HH”VV’];
%
% for i=1:size(pol)
% m=strcat(pol,’GHz’)
% j=strcat(f,m) %i m trying to read file which i hv imported file name is 1GHzHH.tab

% theta=data(:,1);
% phi=data(:,2);
% total=data(:,3);
% for j=1:nume1(theta)
% for k=1:nume1(phi)
%
% end
% end
% endi hv theta value as 60 and 61 each theta value has phi of 120 240 with corresponding total values.
i hv to mirror data that is phi values data from 120 to 180 should be mirrored to 180 to 240 and then its plot is plotted. aim is to get plots symmetry xaxis is phi y axis is total with respect to theta percentile plots.i hv tried with excel sheet but my data is in .tab file how to import that format file and plot from it

a=xlsread(‘1GHzHH.xlsx’);
theta=a(:,1);
phi=a(:,2);
total=a(:,3);
figure
plot(phi,total)
% f=[1];
% pol=[‘HH”VV’];
%
% for i=1:size(pol)
% m=strcat(pol,’GHz’)
% j=strcat(f,m) %i m trying to read file which i hv imported file name is 1GHzHH.tab

% theta=data(:,1);
% phi=data(:,2);
% total=data(:,3);
% for j=1:nume1(theta)
% for k=1:nume1(phi)
%
% end
% end
% end i hv theta value as 60 and 61 each theta value has phi of 120 240 with corresponding total values.
i hv to mirror data that is phi values data from 120 to 180 should be mirrored to 180 to 240 and then its plot is plotted. aim is to get plots symmetry xaxis is phi y axis is total with respect to theta percentile plots.i hv tried with excel sheet but my data is in .tab file how to import that format file and plot from it

a=xlsread(‘1GHzHH.xlsx’);
theta=a(:,1);
phi=a(:,2);
total=a(:,3);
figure
plot(phi,total)
% f=[1];
% pol=[‘HH”VV’];
%
% for i=1:size(pol)
% m=strcat(pol,’GHz’)
% j=strcat(f,m) %i m trying to read file which i hv imported file name is 1GHzHH.tab

% theta=data(:,1);
% phi=data(:,2);
% total=data(:,3);
% for j=1:nume1(theta)
% for k=1:nume1(phi)
%
% end
% end
% end plot MATLAB Answers — New Questions

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