Category: Matlab
Category Archives: Matlab
After adding a command to get dotted line graph I am still getting solid line only in the legend i am getting dotted line
plot(x1 – x2,F,’:g’);
xlabel(‘Displacement(m)’)
ylabel(‘Spring Force(N)’)
legend(‘F’)plot(x1 – x2,F,’:g’);
xlabel(‘Displacement(m)’)
ylabel(‘Spring Force(N)’)
legend(‘F’) plot(x1 – x2,F,’:g’);
xlabel(‘Displacement(m)’)
ylabel(‘Spring Force(N)’)
legend(‘F’) graph MATLAB Answers — New Questions
I have a nonlinear equation with a symbolic variable and cant solve it.
Hi, I have to plot a phase diagram of an implicit equation, but in there lies a function p that depends nonlinearly on one of the variables (alpha), so I am trying to solve the nonlinear equation in terms of my variable alpha. However, I only get a solution in terms of a z2 variable. Can anybody help? Here is the code
n = 2.1;
alpha0 = 0;
syms p alpha
eqn = p^(n+1) – alpha0*p^n + p -(alpha0 + alpha) == 0;
sol = solve(eqn,p);
For n = 2 works just fine, but not for n = 2.1, which prompts my problem.Hi, I have to plot a phase diagram of an implicit equation, but in there lies a function p that depends nonlinearly on one of the variables (alpha), so I am trying to solve the nonlinear equation in terms of my variable alpha. However, I only get a solution in terms of a z2 variable. Can anybody help? Here is the code
n = 2.1;
alpha0 = 0;
syms p alpha
eqn = p^(n+1) – alpha0*p^n + p -(alpha0 + alpha) == 0;
sol = solve(eqn,p);
For n = 2 works just fine, but not for n = 2.1, which prompts my problem. Hi, I have to plot a phase diagram of an implicit equation, but in there lies a function p that depends nonlinearly on one of the variables (alpha), so I am trying to solve the nonlinear equation in terms of my variable alpha. However, I only get a solution in terms of a z2 variable. Can anybody help? Here is the code
n = 2.1;
alpha0 = 0;
syms p alpha
eqn = p^(n+1) – alpha0*p^n + p -(alpha0 + alpha) == 0;
sol = solve(eqn,p);
For n = 2 works just fine, but not for n = 2.1, which prompts my problem. nonlinear, nonlinear equation, symbolic variable MATLAB Answers — New Questions
Resetting only a section of my state during event driven ode45 simulation
Hello,
I’m trying to write a code that simulates a closed loop event driven LTI system (with a poleplacement controller u(t) = -K*x(t)). I’m using the following event formulation:
where the stands for the actual state and the stands for the error between the current state and the state measured during an event trigger. So:
So I want to trigger an event the moment the value becomes 0.
Now, the problem I’m having is that I want the value (so the error) to reset to 0 upon an event but the part of the state should not reset. I’m having trouble coming up with a way to exactly program this. I’m currently just simulating the system dynamics for the augmented system, so for . For now I have the following in matlab:
a = 4;
b = 6;
c = 9;
poles = [-1+2i; -1-2i];
A = [0.3+a-b, 0.5-c; 0, 1];
B = [0; 1];
C = eye(2);
D = [0; 0];
K = place(A, B, poles);
A_aug = [A-B*K -B*K;
-A+B*K B*K];%Defining augmented CL A-matrix for augmented state x_augmented = [x;error]//[xi;epsilon]
tspan = [0,10];
x0 = [1;1;0;0];
Q = eye(2);
P = 2*eye(2);
sig = 0.1;
options = odeset(‘Events’, @(t,x) myEventsFcn(t,x,sig,Q,P,B,K));
[t,y,te,ye,ie] = ode45(@(t,x) LTIaug_fun(t,x,A_aug),tspan,x0,options)
Where the LTIaug_fun and myEventsFcn are defined as follows:
function dxdt = LTIaug_fun(~,x,A_aug)
dxdt = A_aug*x;
end
function [value,isterminal,direction] = myEventsFcn(t,x,sig,Q,P,B,K)
Th = [(1-sig)*Q P*B*K;
(B*K)’*P zeros(2,2)];
Th_e = x’*Th*x;
value = Th_e;
isterminal = 1;
direction = 0;
end
So how do I set my part of my augmented state to 0 after/during an event trigger?Hello,
I’m trying to write a code that simulates a closed loop event driven LTI system (with a poleplacement controller u(t) = -K*x(t)). I’m using the following event formulation:
where the stands for the actual state and the stands for the error between the current state and the state measured during an event trigger. So:
So I want to trigger an event the moment the value becomes 0.
Now, the problem I’m having is that I want the value (so the error) to reset to 0 upon an event but the part of the state should not reset. I’m having trouble coming up with a way to exactly program this. I’m currently just simulating the system dynamics for the augmented system, so for . For now I have the following in matlab:
a = 4;
b = 6;
c = 9;
poles = [-1+2i; -1-2i];
A = [0.3+a-b, 0.5-c; 0, 1];
B = [0; 1];
C = eye(2);
D = [0; 0];
K = place(A, B, poles);
A_aug = [A-B*K -B*K;
-A+B*K B*K];%Defining augmented CL A-matrix for augmented state x_augmented = [x;error]//[xi;epsilon]
tspan = [0,10];
x0 = [1;1;0;0];
Q = eye(2);
P = 2*eye(2);
sig = 0.1;
options = odeset(‘Events’, @(t,x) myEventsFcn(t,x,sig,Q,P,B,K));
[t,y,te,ye,ie] = ode45(@(t,x) LTIaug_fun(t,x,A_aug),tspan,x0,options)
Where the LTIaug_fun and myEventsFcn are defined as follows:
function dxdt = LTIaug_fun(~,x,A_aug)
dxdt = A_aug*x;
end
function [value,isterminal,direction] = myEventsFcn(t,x,sig,Q,P,B,K)
Th = [(1-sig)*Q P*B*K;
(B*K)’*P zeros(2,2)];
Th_e = x’*Th*x;
value = Th_e;
isterminal = 1;
direction = 0;
end
So how do I set my part of my augmented state to 0 after/during an event trigger? Hello,
I’m trying to write a code that simulates a closed loop event driven LTI system (with a poleplacement controller u(t) = -K*x(t)). I’m using the following event formulation:
where the stands for the actual state and the stands for the error between the current state and the state measured during an event trigger. So:
So I want to trigger an event the moment the value becomes 0.
Now, the problem I’m having is that I want the value (so the error) to reset to 0 upon an event but the part of the state should not reset. I’m having trouble coming up with a way to exactly program this. I’m currently just simulating the system dynamics for the augmented system, so for . For now I have the following in matlab:
a = 4;
b = 6;
c = 9;
poles = [-1+2i; -1-2i];
A = [0.3+a-b, 0.5-c; 0, 1];
B = [0; 1];
C = eye(2);
D = [0; 0];
K = place(A, B, poles);
A_aug = [A-B*K -B*K;
-A+B*K B*K];%Defining augmented CL A-matrix for augmented state x_augmented = [x;error]//[xi;epsilon]
tspan = [0,10];
x0 = [1;1;0;0];
Q = eye(2);
P = 2*eye(2);
sig = 0.1;
options = odeset(‘Events’, @(t,x) myEventsFcn(t,x,sig,Q,P,B,K));
[t,y,te,ye,ie] = ode45(@(t,x) LTIaug_fun(t,x,A_aug),tspan,x0,options)
Where the LTIaug_fun and myEventsFcn are defined as follows:
function dxdt = LTIaug_fun(~,x,A_aug)
dxdt = A_aug*x;
end
function [value,isterminal,direction] = myEventsFcn(t,x,sig,Q,P,B,K)
Th = [(1-sig)*Q P*B*K;
(B*K)’*P zeros(2,2)];
Th_e = x’*Th*x;
value = Th_e;
isterminal = 1;
direction = 0;
end
So how do I set my part of my augmented state to 0 after/during an event trigger? ode45, event based control MATLAB Answers — New Questions
ADI method for 2D reaction diffusion equation
hi , i want to use ADI mathod to fisherkpp equation and calcultae the integral between t=0 and t=10 however im not sure that i apply the method correctly ? so i want your feedbacks and your suggestion sto improve the code
here the code
% Parameters
Lx = 60; % Length of domain in x-direction
Ly = 1; % Length of domain in y-direction
Nx = 600; % Number of grid points in x-direction
Ny = 10; % Number of grid points in y-direction
final_time = 10; % Final time
D = 1; % Diffusion coefficient
r = 1; % Growth rate
% Discretization
dx = Lx / (Nx – 1);
dy = Ly / (Ny – 1);
dt = 0.006; % Time step
Nt = 5500; % Number of time steps
x = linspace(0, Lx, Nx);
y = linspace(0, Ly, Ny);
% Initial condition
[X, Y] = meshgrid(x, y);
u0 = (1/2 – 1/2 * tanh(1/(2*sqrt(6)) * X)).^2;
% ADI method
alpha = D * dt / dx^2;
beta = D * dt / dy^2;
gamma = r * dt;
u = u0;
u_new = u;
integral_u = 0;
for t = 1:Nt
% Implicit step in x-direction
for j = 2:Ny-1
% Construct tridiagonal matrix for x-direction
A = -alpha * ones(1, Nx-3);
B = (1 + 2 * alpha) * ones(1, Nx-2);
C = -alpha * ones(1, Nx-3);
D_vec = u(j, 2:end-1) + gamma * u(j, 2:end-1) .* (1 – u(j, 2:end-1));
% Solve tridiagonal system
u_new(j, 2:end-1) = tridiag_solve(A, B, C, D_vec);
end
% Implicit step in y-direction
for i = 2:Nx-1
% Construct tridiagonal matrix for y-direction
A = -beta * ones(1, Ny-3);
B = (1 + 2 * beta) * ones(1, Ny-2);
C = -beta * ones(1, Ny-3);
D_vec = u_new(2:end-1, i) + gamma * u_new(2:end-1, i) .* (1 – u_new(2:end-1, i));
% Solve tridiagonal system
u_new(2:end-1, i) = tridiag_solve(A, B, C, D_vec);
end
% Update integral of u over domain
if t > 1
integral_u = integral_u + trapz(y, trapz(x, u_new, 2), 1) * dt; % Trapezoidal numerical integration
end
u = u_new;
end
disp([‘Integral of u(x, y) over domain between t=0 and final time: ‘, num2str(integral_u)]);
function x = tridiag_solve(A, B, C, D)
% Solves a tridiagonal system of equations Ax = D
N = length(B);
c = zeros(N-1, 1);
d = zeros(N, 1);
x = zeros(N, 1);
c(1) = C(1) / B(1);
d(1) = D(1) / B(1);
for i = 2:N-1
denom = B(i) – A(i-1) * c(i-1);
c(i) = C(i) / denom;
d(i) = (D(i) – A(i-1) * d(i-1)) / denom;
end
d(N) = (D(N) – A(N-1) * d(N-1)) / (B(N) – A(N-1) * c(N-1));
x(N) = d(N);
for i = N-1:-1:1
x(i) = d(i) – c(i) * x(i+1);
end
endhi , i want to use ADI mathod to fisherkpp equation and calcultae the integral between t=0 and t=10 however im not sure that i apply the method correctly ? so i want your feedbacks and your suggestion sto improve the code
here the code
% Parameters
Lx = 60; % Length of domain in x-direction
Ly = 1; % Length of domain in y-direction
Nx = 600; % Number of grid points in x-direction
Ny = 10; % Number of grid points in y-direction
final_time = 10; % Final time
D = 1; % Diffusion coefficient
r = 1; % Growth rate
% Discretization
dx = Lx / (Nx – 1);
dy = Ly / (Ny – 1);
dt = 0.006; % Time step
Nt = 5500; % Number of time steps
x = linspace(0, Lx, Nx);
y = linspace(0, Ly, Ny);
% Initial condition
[X, Y] = meshgrid(x, y);
u0 = (1/2 – 1/2 * tanh(1/(2*sqrt(6)) * X)).^2;
% ADI method
alpha = D * dt / dx^2;
beta = D * dt / dy^2;
gamma = r * dt;
u = u0;
u_new = u;
integral_u = 0;
for t = 1:Nt
% Implicit step in x-direction
for j = 2:Ny-1
% Construct tridiagonal matrix for x-direction
A = -alpha * ones(1, Nx-3);
B = (1 + 2 * alpha) * ones(1, Nx-2);
C = -alpha * ones(1, Nx-3);
D_vec = u(j, 2:end-1) + gamma * u(j, 2:end-1) .* (1 – u(j, 2:end-1));
% Solve tridiagonal system
u_new(j, 2:end-1) = tridiag_solve(A, B, C, D_vec);
end
% Implicit step in y-direction
for i = 2:Nx-1
% Construct tridiagonal matrix for y-direction
A = -beta * ones(1, Ny-3);
B = (1 + 2 * beta) * ones(1, Ny-2);
C = -beta * ones(1, Ny-3);
D_vec = u_new(2:end-1, i) + gamma * u_new(2:end-1, i) .* (1 – u_new(2:end-1, i));
% Solve tridiagonal system
u_new(2:end-1, i) = tridiag_solve(A, B, C, D_vec);
end
% Update integral of u over domain
if t > 1
integral_u = integral_u + trapz(y, trapz(x, u_new, 2), 1) * dt; % Trapezoidal numerical integration
end
u = u_new;
end
disp([‘Integral of u(x, y) over domain between t=0 and final time: ‘, num2str(integral_u)]);
function x = tridiag_solve(A, B, C, D)
% Solves a tridiagonal system of equations Ax = D
N = length(B);
c = zeros(N-1, 1);
d = zeros(N, 1);
x = zeros(N, 1);
c(1) = C(1) / B(1);
d(1) = D(1) / B(1);
for i = 2:N-1
denom = B(i) – A(i-1) * c(i-1);
c(i) = C(i) / denom;
d(i) = (D(i) – A(i-1) * d(i-1)) / denom;
end
d(N) = (D(N) – A(N-1) * d(N-1)) / (B(N) – A(N-1) * c(N-1));
x(N) = d(N);
for i = N-1:-1:1
x(i) = d(i) – c(i) * x(i+1);
end
end hi , i want to use ADI mathod to fisherkpp equation and calcultae the integral between t=0 and t=10 however im not sure that i apply the method correctly ? so i want your feedbacks and your suggestion sto improve the code
here the code
% Parameters
Lx = 60; % Length of domain in x-direction
Ly = 1; % Length of domain in y-direction
Nx = 600; % Number of grid points in x-direction
Ny = 10; % Number of grid points in y-direction
final_time = 10; % Final time
D = 1; % Diffusion coefficient
r = 1; % Growth rate
% Discretization
dx = Lx / (Nx – 1);
dy = Ly / (Ny – 1);
dt = 0.006; % Time step
Nt = 5500; % Number of time steps
x = linspace(0, Lx, Nx);
y = linspace(0, Ly, Ny);
% Initial condition
[X, Y] = meshgrid(x, y);
u0 = (1/2 – 1/2 * tanh(1/(2*sqrt(6)) * X)).^2;
% ADI method
alpha = D * dt / dx^2;
beta = D * dt / dy^2;
gamma = r * dt;
u = u0;
u_new = u;
integral_u = 0;
for t = 1:Nt
% Implicit step in x-direction
for j = 2:Ny-1
% Construct tridiagonal matrix for x-direction
A = -alpha * ones(1, Nx-3);
B = (1 + 2 * alpha) * ones(1, Nx-2);
C = -alpha * ones(1, Nx-3);
D_vec = u(j, 2:end-1) + gamma * u(j, 2:end-1) .* (1 – u(j, 2:end-1));
% Solve tridiagonal system
u_new(j, 2:end-1) = tridiag_solve(A, B, C, D_vec);
end
% Implicit step in y-direction
for i = 2:Nx-1
% Construct tridiagonal matrix for y-direction
A = -beta * ones(1, Ny-3);
B = (1 + 2 * beta) * ones(1, Ny-2);
C = -beta * ones(1, Ny-3);
D_vec = u_new(2:end-1, i) + gamma * u_new(2:end-1, i) .* (1 – u_new(2:end-1, i));
% Solve tridiagonal system
u_new(2:end-1, i) = tridiag_solve(A, B, C, D_vec);
end
% Update integral of u over domain
if t > 1
integral_u = integral_u + trapz(y, trapz(x, u_new, 2), 1) * dt; % Trapezoidal numerical integration
end
u = u_new;
end
disp([‘Integral of u(x, y) over domain between t=0 and final time: ‘, num2str(integral_u)]);
function x = tridiag_solve(A, B, C, D)
% Solves a tridiagonal system of equations Ax = D
N = length(B);
c = zeros(N-1, 1);
d = zeros(N, 1);
x = zeros(N, 1);
c(1) = C(1) / B(1);
d(1) = D(1) / B(1);
for i = 2:N-1
denom = B(i) – A(i-1) * c(i-1);
c(i) = C(i) / denom;
d(i) = (D(i) – A(i-1) * d(i-1)) / denom;
end
d(N) = (D(N) – A(N-1) * d(N-1)) / (B(N) – A(N-1) * c(N-1));
x(N) = d(N);
for i = N-1:-1:1
x(i) = d(i) – c(i) * x(i+1);
end
end numerical integration, numerical analysis MATLAB Answers — New Questions
How may i simulate a PMSM machine as a charge, for example a resistance?
My simulation is obtain a graph of behaviour in charge and discharge of a pack of supercapacitors for a 186kw of charge (motor).My simulation is obtain a graph of behaviour in charge and discharge of a pack of supercapacitors for a 186kw of charge (motor). My simulation is obtain a graph of behaviour in charge and discharge of a pack of supercapacitors for a 186kw of charge (motor). charge and discharge of supercapacitors MATLAB Answers — New Questions
How to use Solver in Matlab?
Hi,
Im optimising prices based on excel formulas. I have constructed a complete set of financial statements which is all linked to many cells. Can I upload that excel file to Matlab and work on Solver?
I have Objective cell, Change variable cells and Constraints in excel’s solver. Can someone guide me how to use non linear solver in matlab please? Im using Web Matlab.Hi,
Im optimising prices based on excel formulas. I have constructed a complete set of financial statements which is all linked to many cells. Can I upload that excel file to Matlab and work on Solver?
I have Objective cell, Change variable cells and Constraints in excel’s solver. Can someone guide me how to use non linear solver in matlab please? Im using Web Matlab. Hi,
Im optimising prices based on excel formulas. I have constructed a complete set of financial statements which is all linked to many cells. Can I upload that excel file to Matlab and work on Solver?
I have Objective cell, Change variable cells and Constraints in excel’s solver. Can someone guide me how to use non linear solver in matlab please? Im using Web Matlab. solver, matlab MATLAB Answers — New Questions
Trainnet with parallel-CPU mode giving incorrect results
I’m using trainnet to train a convolutional regression network to find the X-Y centroid of a subtle gradient region in an input image. The training data consist of paired 130×326 grayscale images and ground-truth output coordinates. Both the RMSE and loss function reach very small numbers (eg 10^-3) after a few minutes of training on a smal dataset. The trained network gives the expected results when trained in single-CPU mode, but when trained in parallel-CPU mode, the predictions are significantly off. To attempt debugging, I scaled back to a very simple network, disabled normalization, and trained with only two datapoints–fully expecting it to memorize the training data perfectly. Using single-CPU training mode, the trained network yields perfect predictions (as expected) on the training data, but after using parallel-CPU mode, the trained network does not predict correctly on the training data. I added in a more verbose loss function and confirmed that the reported losses (i.e. showin in the loss function during training) are consistent with the (Y,T) pairs during training, and that the T values are being correctly read from the training data.
It seems perhaps the final outputted network in parallel-CPU mode does not correcltly capture the results of the training.
I’m running 2024a on a MBPro (M2 Max), using Apple Accelerate BLAS. (Default BLAS persistently crashed in parallel mode with trainnet.)
Code snippet below…
layers = [
imageInputLayer([130 326 1],"Name","imageinput","Normalization","none")
convolution2dLayer([10 10],8,"dilation",[2 2],"Name","conv_1")
maxPooling2dLayer([2 2],"Name","maxpool_4")
batchNormalizationLayer
reluLayer("Name","relu_1")
convolution2dLayer([2 2],16,"Name","conv_2")
fullyConnectedLayer(2,"Name","fc")];
opts = trainingOptions(‘sgdm’, …
‘InitialLearnRate’,1e-7, …
‘LearnRateSchedule’,’piecewise’,…
‘LearnRateDropPeriod’,500,…
‘LearnRateDropFactor’,.25,…
‘MaxEpochs’,1000, …
‘Verbose’,false, …
‘ExecutionEnvironment’,’parallel’,…
‘Shuffle’,’every-epoch’,…
‘Plots’,’training-progress’, …
‘OutputNetwork’,’last-iteration’);
FOVCnet = trainnet(trainingData,net,@modelLoss,opts);
function loss = modelLoss(Y,T) % define loss function
Y
T
loss = mse(Y,T)
endI’m using trainnet to train a convolutional regression network to find the X-Y centroid of a subtle gradient region in an input image. The training data consist of paired 130×326 grayscale images and ground-truth output coordinates. Both the RMSE and loss function reach very small numbers (eg 10^-3) after a few minutes of training on a smal dataset. The trained network gives the expected results when trained in single-CPU mode, but when trained in parallel-CPU mode, the predictions are significantly off. To attempt debugging, I scaled back to a very simple network, disabled normalization, and trained with only two datapoints–fully expecting it to memorize the training data perfectly. Using single-CPU training mode, the trained network yields perfect predictions (as expected) on the training data, but after using parallel-CPU mode, the trained network does not predict correctly on the training data. I added in a more verbose loss function and confirmed that the reported losses (i.e. showin in the loss function during training) are consistent with the (Y,T) pairs during training, and that the T values are being correctly read from the training data.
It seems perhaps the final outputted network in parallel-CPU mode does not correcltly capture the results of the training.
I’m running 2024a on a MBPro (M2 Max), using Apple Accelerate BLAS. (Default BLAS persistently crashed in parallel mode with trainnet.)
Code snippet below…
layers = [
imageInputLayer([130 326 1],"Name","imageinput","Normalization","none")
convolution2dLayer([10 10],8,"dilation",[2 2],"Name","conv_1")
maxPooling2dLayer([2 2],"Name","maxpool_4")
batchNormalizationLayer
reluLayer("Name","relu_1")
convolution2dLayer([2 2],16,"Name","conv_2")
fullyConnectedLayer(2,"Name","fc")];
opts = trainingOptions(‘sgdm’, …
‘InitialLearnRate’,1e-7, …
‘LearnRateSchedule’,’piecewise’,…
‘LearnRateDropPeriod’,500,…
‘LearnRateDropFactor’,.25,…
‘MaxEpochs’,1000, …
‘Verbose’,false, …
‘ExecutionEnvironment’,’parallel’,…
‘Shuffle’,’every-epoch’,…
‘Plots’,’training-progress’, …
‘OutputNetwork’,’last-iteration’);
FOVCnet = trainnet(trainingData,net,@modelLoss,opts);
function loss = modelLoss(Y,T) % define loss function
Y
T
loss = mse(Y,T)
end I’m using trainnet to train a convolutional regression network to find the X-Y centroid of a subtle gradient region in an input image. The training data consist of paired 130×326 grayscale images and ground-truth output coordinates. Both the RMSE and loss function reach very small numbers (eg 10^-3) after a few minutes of training on a smal dataset. The trained network gives the expected results when trained in single-CPU mode, but when trained in parallel-CPU mode, the predictions are significantly off. To attempt debugging, I scaled back to a very simple network, disabled normalization, and trained with only two datapoints–fully expecting it to memorize the training data perfectly. Using single-CPU training mode, the trained network yields perfect predictions (as expected) on the training data, but after using parallel-CPU mode, the trained network does not predict correctly on the training data. I added in a more verbose loss function and confirmed that the reported losses (i.e. showin in the loss function during training) are consistent with the (Y,T) pairs during training, and that the T values are being correctly read from the training data.
It seems perhaps the final outputted network in parallel-CPU mode does not correcltly capture the results of the training.
I’m running 2024a on a MBPro (M2 Max), using Apple Accelerate BLAS. (Default BLAS persistently crashed in parallel mode with trainnet.)
Code snippet below…
layers = [
imageInputLayer([130 326 1],"Name","imageinput","Normalization","none")
convolution2dLayer([10 10],8,"dilation",[2 2],"Name","conv_1")
maxPooling2dLayer([2 2],"Name","maxpool_4")
batchNormalizationLayer
reluLayer("Name","relu_1")
convolution2dLayer([2 2],16,"Name","conv_2")
fullyConnectedLayer(2,"Name","fc")];
opts = trainingOptions(‘sgdm’, …
‘InitialLearnRate’,1e-7, …
‘LearnRateSchedule’,’piecewise’,…
‘LearnRateDropPeriod’,500,…
‘LearnRateDropFactor’,.25,…
‘MaxEpochs’,1000, …
‘Verbose’,false, …
‘ExecutionEnvironment’,’parallel’,…
‘Shuffle’,’every-epoch’,…
‘Plots’,’training-progress’, …
‘OutputNetwork’,’last-iteration’);
FOVCnet = trainnet(trainingData,net,@modelLoss,opts);
function loss = modelLoss(Y,T) % define loss function
Y
T
loss = mse(Y,T)
end trainnet, parallel-cpu, regression, macos MATLAB Answers — New Questions
Curve Fitting for a Rational Polynomial Model.
Dear all,
I want to find the best rational Polynomial model that can fit the data shown.
any help would be appreciated.
Data are in dB.Dear all,
I want to find the best rational Polynomial model that can fit the data shown.
any help would be appreciated.
Data are in dB. Dear all,
I want to find the best rational Polynomial model that can fit the data shown.
any help would be appreciated.
Data are in dB. curve fitting MATLAB Answers — New Questions
