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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Hello! i have been to run this model fitting code but keeps on getting these error messages as:
FUN must have two input arguments.
userFcn_ME = initEvalErrorHandler(userFcn_ME,funfcn_x_xdata{3}, …
[params_fit, ~] = lsqcurvefit(obj_fun, params0, time, observed_I, [], [], options);
Find attached, the code,, Please help

function dydt = sir_model(t, y, beta, gamma)
S = y(1);
I = y(2);
R = y(3);
N = S + I + R; % Total population

dSdt = -beta * S * I / N;
dIdt = beta * S * I / N – gamma * I;
dRdt = gamma * I;

dydt = [dSdt; dIdt; dRdt];
end

function error = model_error(params, time, observed_I, y0)
beta = params(1);
gamma = params(2);

% Solve the differential equations
[~, y] = ode45(@(t, y) sir_model(t, y, beta, gamma), time, y0);

% Extract the infectious data from the solution
model_I = y(:, 2);

% Compute the error as the difference between model and observed data
error = model_I – observed_I’;
end

% Define the time vector and observed data
time = [0, 1, 2, 3, 4, 5]; % Example time points
observed_I = [10, 15, 20, 25, 30, 35]; % Example observed infectious data

% Initial guess for parameters
beta_guess = 0.3;
gamma_guess = 0.1;
params0 = [beta_guess, gamma_guess];

% Initial conditions
S0 = 1000; % Initial number of susceptible individuals
I0 = observed_I(1); % Initial number of infectious individuals
R0 = 0; % Initial number of recovered individuals
y0 = [S0; I0; R0];

% Define the objective function for fitting
obj_fun = @(params) model_error(params, time, observed_I, y0);

% Perform the curve fitting
options = optimoptions(‘lsqcurvefit’, ‘Display’, ‘off’);
[params_fit, ~] = lsqcurvefit(obj_fun, params0, time, observed_I, [], [], options);

% Display the fitted parameters
beta_fit = params_fit(1);
gamma_fit = params_fit(2);
disp([‘Fitted beta: ‘, num2str(beta_fit)]);
disp([‘Fitted gamma: ‘, num2str(gamma_fit)]);Hello! i have been to run this model fitting code but keeps on getting these error messages as:
FUN must have two input arguments.
userFcn_ME = initEvalErrorHandler(userFcn_ME,funfcn_x_xdata{3}, …
[params_fit, ~] = lsqcurvefit(obj_fun, params0, time, observed_I, [], [], options);
Find attached, the code,, Please help

function dydt = sir_model(t, y, beta, gamma)
S = y(1);
I = y(2);
R = y(3);
N = S + I + R; % Total population

dSdt = -beta * S * I / N;
dIdt = beta * S * I / N – gamma * I;
dRdt = gamma * I;

dydt = [dSdt; dIdt; dRdt];
end

function error = model_error(params, time, observed_I, y0)
beta = params(1);
gamma = params(2);

% Solve the differential equations
[~, y] = ode45(@(t, y) sir_model(t, y, beta, gamma), time, y0);

% Extract the infectious data from the solution
model_I = y(:, 2);

% Compute the error as the difference between model and observed data
error = model_I – observed_I’;
end

% Define the time vector and observed data
time = [0, 1, 2, 3, 4, 5]; % Example time points
observed_I = [10, 15, 20, 25, 30, 35]; % Example observed infectious data

% Initial guess for parameters
beta_guess = 0.3;
gamma_guess = 0.1;
params0 = [beta_guess, gamma_guess];

% Initial conditions
S0 = 1000; % Initial number of susceptible individuals
I0 = observed_I(1); % Initial number of infectious individuals
R0 = 0; % Initial number of recovered individuals
y0 = [S0; I0; R0];

% Define the objective function for fitting
obj_fun = @(params) model_error(params, time, observed_I, y0);

% Perform the curve fitting
options = optimoptions(‘lsqcurvefit’, ‘Display’, ‘off’);
[params_fit, ~] = lsqcurvefit(obj_fun, params0, time, observed_I, [], [], options);

% Display the fitted parameters
beta_fit = params_fit(1);
gamma_fit = params_fit(2);
disp([‘Fitted beta: ‘, num2str(beta_fit)]);
disp([‘Fitted gamma: ‘, num2str(gamma_fit)]); Hello! i have been to run this model fitting code but keeps on getting these error messages as:
FUN must have two input arguments.
userFcn_ME = initEvalErrorHandler(userFcn_ME,funfcn_x_xdata{3}, …
[params_fit, ~] = lsqcurvefit(obj_fun, params0, time, observed_I, [], [], options);
Find attached, the code,, Please help

function dydt = sir_model(t, y, beta, gamma)
S = y(1);
I = y(2);
R = y(3);
N = S + I + R; % Total population

dSdt = -beta * S * I / N;
dIdt = beta * S * I / N – gamma * I;
dRdt = gamma * I;

dydt = [dSdt; dIdt; dRdt];
end

function error = model_error(params, time, observed_I, y0)
beta = params(1);
gamma = params(2);

% Solve the differential equations
[~, y] = ode45(@(t, y) sir_model(t, y, beta, gamma), time, y0);

% Extract the infectious data from the solution
model_I = y(:, 2);

% Compute the error as the difference between model and observed data
error = model_I – observed_I’;
end

% Define the time vector and observed data
time = [0, 1, 2, 3, 4, 5]; % Example time points
observed_I = [10, 15, 20, 25, 30, 35]; % Example observed infectious data

% Initial guess for parameters
beta_guess = 0.3;
gamma_guess = 0.1;
params0 = [beta_guess, gamma_guess];

% Initial conditions
S0 = 1000; % Initial number of susceptible individuals
I0 = observed_I(1); % Initial number of infectious individuals
R0 = 0; % Initial number of recovered individuals
y0 = [S0; I0; R0];

% Define the objective function for fitting
obj_fun = @(params) model_error(params, time, observed_I, y0);

% Perform the curve fitting
options = optimoptions(‘lsqcurvefit’, ‘Display’, ‘off’);
[params_fit, ~] = lsqcurvefit(obj_fun, params0, time, observed_I, [], [], options);

% Display the fitted parameters
beta_fit = params_fit(1);
gamma_fit = params_fit(2);
disp([‘Fitted beta: ‘, num2str(beta_fit)]);
disp([‘Fitted gamma: ‘, num2str(gamma_fit)]); model fitting MATLAB Answers — New Questions

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Hello. I have created a custom S Function block that outputs the sum of a user definable number of inputs and it works in simulation. The next step for me is to generate C code from it using Simulink Coder. For now I don’t want to inline the S function, I just want to test its autocoded functionality.
When I generate the src and header files and try to build an executable in eclipse I get the following errors:
unknown type name ‘RTWSfcnInfo’ in mymodel.h
unknown type name ‘mxarray’ in mymodel.h,simstruct_def.h, simstruct_internal.h
unknown type name ‘_ResolveVarFcn’ ini simstruct_def.h
#error unrecognized use in simstruct_compond.h
#error unhandled case in simstruct_compond.h
#error Must define one of RT, NRT, MATLAB_MEX_FILE, SL_INTERNAL, or FIPXT_SHARED_MODULE in simstruc_compcond.h
I have looked for a solution online and for other people this was solved by including mex.h – I have tried it and it did not work.

Thanks in advance!Hello. I have created a custom S Function block that outputs the sum of a user definable number of inputs and it works in simulation. The next step for me is to generate C code from it using Simulink Coder. For now I don’t want to inline the S function, I just want to test its autocoded functionality.
When I generate the src and header files and try to build an executable in eclipse I get the following errors:
unknown type name ‘RTWSfcnInfo’ in mymodel.h
unknown type name ‘mxarray’ in mymodel.h,simstruct_def.h, simstruct_internal.h
unknown type name ‘_ResolveVarFcn’ ini simstruct_def.h
#error unrecognized use in simstruct_compond.h
#error unhandled case in simstruct_compond.h
#error Must define one of RT, NRT, MATLAB_MEX_FILE, SL_INTERNAL, or FIPXT_SHARED_MODULE in simstruc_compcond.h
I have looked for a solution online and for other people this was solved by including mex.h – I have tried it and it did not work.

Thanks in advance! Hello. I have created a custom S Function block that outputs the sum of a user definable number of inputs and it works in simulation. The next step for me is to generate C code from it using Simulink Coder. For now I don’t want to inline the S function, I just want to test its autocoded functionality.
When I generate the src and header files and try to build an executable in eclipse I get the following errors:
unknown type name ‘RTWSfcnInfo’ in mymodel.h
unknown type name ‘mxarray’ in mymodel.h,simstruct_def.h, simstruct_internal.h
unknown type name ‘_ResolveVarFcn’ ini simstruct_def.h
#error unrecognized use in simstruct_compond.h
#error unhandled case in simstruct_compond.h
#error Must define one of RT, NRT, MATLAB_MEX_FILE, SL_INTERNAL, or FIPXT_SHARED_MODULE in simstruc_compcond.h
I have looked for a solution online and for other people this was solved by including mex.h – I have tried it and it did not work.

Thanks in advance! simulink, embedded coder, s-function 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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