Hello All
I am using the Industrial Robot Models in Simscape by Steve Miller, and need help editing a robot model. Specifically, I would like to modify link dimensions, add joints, or adjust dynamics. Any guidance or tips would be greatly appreciated!
Thank you in advance
ChintanHello All
I am using the Industrial Robot Models in Simscape by Steve Miller, and need help editing a robot model. Specifically, I would like to modify link dimensions, add joints, or adjust dynamics. Any guidance or tips would be greatly appreciated!
Thank you in advance
Chintan Hello All
I am using the Industrial Robot Models in Simscape by Steve Miller, and need help editing a robot model. Specifically, I would like to modify link dimensions, add joints, or adjust dynamics. Any guidance or tips would be greatly appreciated!
Thank you in advance
Chintan industrial robot models, robotic manipulator, 6-dof pick and place manipulator, simscape model MATLAB Answers — New Questions

​

I have a Simulink model that uses variables set in Matlab code. However it keeps giving me the same error.
I have this function
function y = tclabsim(t, x0, u, p)
% Ensure input sizes match
if length(t) ~= length(u)
error(‘Time vector t and input profile u must have the same length.’);
end

% Extract parameters from p
U = p(1); % Heat transfer coefficient
alpha = p(2); % Ambient heat loss parameter
tau = p(3); % Time constant

% Initial conditions
x0_T = x0; % Initial temperature

% Ensure t and u are column vectors
t = t(:);
u = u(:);

% Create input matrices
inputMatrix = [t, u];
U_matrix = [0, U];
alpha_matrix = [0, alpha];
tau_matrix = [0, tau];
x0_T_matrix = [0, x0_T];

% Set simulation stop time
stopTime = t(end);

% Assign all required variables to base workspace
assignin(‘base’, ‘x0_T’, x0_T_matrix);
assignin(‘base’, ‘U’, U_matrix);
assignin(‘base’, ‘alpha’, alpha_matrix);
assignin(‘base’, ‘tau’, tau_matrix);
assignin(‘base’, ‘inputMatrix’, inputMatrix);

% Run the Simulink model
simOut = sim(‘SingleHeaterTCLab_1.slx’, ‘SrcWorkspace’, ‘current’, …
‘StopTime’, num2str(stopTime));

% Extract the output from the simulation
y = simOut.Ts; % Sensor temperature output
end
And I call the function like this
% Time vector (0 to 1000 seconds with 1-second steps)
t = (0:999)’; % Make sure t is a column vector

% Initial temperature (20°C)
x0 = 20;

% Heater input (step response: 50% heater power after 100s)
u = zeros(size(t));
u(t >= 100) = 50;

% Parameters [U, alpha, tau]
p = [0.01, 0.01, 200]; % Example values for U, alpha, tau

% Simulate the system
y = tclabsim(t, x0, u, p);
However, every time I try to run it gives the same error.

I have tried to load them as simple double and I have also tried a matrix with t on the first column and the same value in every line of the second column. They also gave the same error.
The block in Simulink has these properties. And I have also tried to change the sample time to -1.

This is the entire simulink if it helps.

How can I fix it?
Thank youI have a Simulink model that uses variables set in Matlab code. However it keeps giving me the same error.
I have this function
function y = tclabsim(t, x0, u, p)
% Ensure input sizes match
if length(t) ~= length(u)
error(‘Time vector t and input profile u must have the same length.’);
end

% Extract parameters from p
U = p(1); % Heat transfer coefficient
alpha = p(2); % Ambient heat loss parameter
tau = p(3); % Time constant

% Initial conditions
x0_T = x0; % Initial temperature

% Ensure t and u are column vectors
t = t(:);
u = u(:);

% Create input matrices
inputMatrix = [t, u];
U_matrix = [0, U];
alpha_matrix = [0, alpha];
tau_matrix = [0, tau];
x0_T_matrix = [0, x0_T];

% Set simulation stop time
stopTime = t(end);

% Assign all required variables to base workspace
assignin(‘base’, ‘x0_T’, x0_T_matrix);
assignin(‘base’, ‘U’, U_matrix);
assignin(‘base’, ‘alpha’, alpha_matrix);
assignin(‘base’, ‘tau’, tau_matrix);
assignin(‘base’, ‘inputMatrix’, inputMatrix);

% Run the Simulink model
simOut = sim(‘SingleHeaterTCLab_1.slx’, ‘SrcWorkspace’, ‘current’, …
‘StopTime’, num2str(stopTime));

% Extract the output from the simulation
y = simOut.Ts; % Sensor temperature output
end
And I call the function like this
% Time vector (0 to 1000 seconds with 1-second steps)
t = (0:999)’; % Make sure t is a column vector

% Initial temperature (20°C)
x0 = 20;

% Heater input (step response: 50% heater power after 100s)
u = zeros(size(t));
u(t >= 100) = 50;

% Parameters [U, alpha, tau]
p = [0.01, 0.01, 200]; % Example values for U, alpha, tau

% Simulate the system
y = tclabsim(t, x0, u, p);
However, every time I try to run it gives the same error.

I have tried to load them as simple double and I have also tried a matrix with t on the first column and the same value in every line of the second column. They also gave the same error.
The block in Simulink has these properties. And I have also tried to change the sample time to -1.

This is the entire simulink if it helps.

How can I fix it?
Thank you I have a Simulink model that uses variables set in Matlab code. However it keeps giving me the same error.
I have this function
function y = tclabsim(t, x0, u, p)
% Ensure input sizes match
if length(t) ~= length(u)
error(‘Time vector t and input profile u must have the same length.’);
end

% Extract parameters from p
U = p(1); % Heat transfer coefficient
alpha = p(2); % Ambient heat loss parameter
tau = p(3); % Time constant

% Initial conditions
x0_T = x0; % Initial temperature

% Ensure t and u are column vectors
t = t(:);
u = u(:);

% Create input matrices
inputMatrix = [t, u];
U_matrix = [0, U];
alpha_matrix = [0, alpha];
tau_matrix = [0, tau];
x0_T_matrix = [0, x0_T];

% Set simulation stop time
stopTime = t(end);

% Assign all required variables to base workspace
assignin(‘base’, ‘x0_T’, x0_T_matrix);
assignin(‘base’, ‘U’, U_matrix);
assignin(‘base’, ‘alpha’, alpha_matrix);
assignin(‘base’, ‘tau’, tau_matrix);
assignin(‘base’, ‘inputMatrix’, inputMatrix);

% Run the Simulink model
simOut = sim(‘SingleHeaterTCLab_1.slx’, ‘SrcWorkspace’, ‘current’, …
‘StopTime’, num2str(stopTime));

% Extract the output from the simulation
y = simOut.Ts; % Sensor temperature output
end
And I call the function like this
% Time vector (0 to 1000 seconds with 1-second steps)
t = (0:999)’; % Make sure t is a column vector

% Initial temperature (20°C)
x0 = 20;

% Heater input (step response: 50% heater power after 100s)
u = zeros(size(t));
u(t >= 100) = 50;

% Parameters [U, alpha, tau]
p = [0.01, 0.01, 200]; % Example values for U, alpha, tau

% Simulate the system
y = tclabsim(t, x0, u, p);
However, every time I try to run it gives the same error.

I have tried to load them as simple double and I have also tried a matrix with t on the first column and the same value in every line of the second column. They also gave the same error.
The block in Simulink has these properties. And I have also tried to change the sample time to -1.

This is the entire simulink if it helps.

How can I fix it?
Thank you simulink, matlab, workspace MATLAB Answers — New Questions

​

I’m working on a MATLAB project where I need to analyze and plot the relative difference in coverage area for different QAM orders and semi-angles (ϕ_1/2) for a wireless communication simulation. I’ve encountered two persistent errors:
Error using xticks (line 33)
Value must be a numeric vector whose values increase.
Error in untitled11 (line 84)
xticks(QAM_order); % Ensure QAM_order is numeric and sorted
and One more thing is the legend has multiples 15, 30, 45 as shown in the below image

Expected Outcome:
Correct x-axis ticks: The x-axis should display the QAM orders {16,32,64,128,256,512,1024}.
Unique legend entries: Each angle (ϕ1/2 =15 ∘ ,30 ∘ ,45 ∘) should appear exactly once in the legend.
Any assistance, please?
here is below my code:

clear variables;
close all;
clc;

%% Define angles and file paths
angles = [15, 30, 45]; % Define angles
angleLabels = ["15°", "30°", "45°"]; % Labels for legends
numAngles = length(angles);

% Load data from the first angle to initialize QAM_order
load([‘workspace_withBeamsteering_’, num2str(angles(1)), ‘.mat’]); % Replace with actual file path
QAM_order = unique(sort(double(QAM_order(:)))); % Ensure QAM_order is numeric, sorted, and unique
numQAMOrders = length(QAM_order); % Dynamically determine number of QAM orders

% Debug: Display QAM_order
disp(‘QAM_order:’);
disp(QAM_order);

% Initialize storage for results
coverageRelativeDiffAll = zeros(numAngles, numQAMOrders); % Initialize storage for relative differences

%% Process data for each angle
for a = 1:numAngles
angle = angles(a);

% Load data for the perfect beamsteering case
load([‘workspace_withBeamsteering_’, num2str(angle), ‘.mat’]); % Replace with actual file path
berMapPerfectCase = berMap;
coveragePerfectCase = coverage;

% Load data for the beamsteering with misalignment case
load([‘workspace_’, num2str(angle), ‘_withBeamsteering_Misalignment.mat’]); % Replace with actual file path

% Compute relative difference in coverage area for this angle
for q = 1:numQAMOrders
% Ensure correct size for coverageMap
[N_directions, N_rx, N_ry] = size(berMap{q});
coverageMap = zeros(N_rx, N_ry);

% Accumulate coverage for all directions
for i_n = 1:N_directions
currentCoverage = squeeze(berMap{q}(i_n, :, :)) < 3.8e-3;
coverageMap = coverageMap + double(currentCoverage);
end

% Compute the intersection map (points covered in all directions)
intersectionMap = (coverageMap == N_directions);

% Calculate misalignment coverage
coverageMisalignment = sum(intersectionMap(:)) * (L * W) / numel(intersectionMap);

% Compute relative difference
coverageRelativeDiffAll(a, q) = (coverageMisalignment – coveragePerfectCase(q)) / coveragePerfectCase(q);
end
end

%% Plot Relative Difference in Coverage Area for All Angles
figure;
hold on;

% Define markers and colors for each angle
markers = {‘-o’, ‘-s’, ‘-^’}; % Different markers for angles
colors = {‘b’, ‘r’, ‘g’}; % Blue, Red, Green

for a = 1:numAngles
plot(QAM_order, coverageRelativeDiffAll(a, 🙂 * 100, …
markers{a}, ‘LineWidth’, 1.5, ‘Color’, colors{a}, …
‘DisplayName’, [‘$phi_{1/2} = $ ‘, num2str(angles(a)), ‘$^circ$’]);
end

hold off;

% Add labels, title, and legend
xlabel(‘QAM Order (M)’, ‘FontSize’, 12, ‘FontWeight’, ‘bold’);
ylabel(‘Relative Difference in Coverage Area (%)’, ‘FontSize’, 12, ‘FontWeight’, ‘bold’);
title(‘Relative Difference in Coverage Area vs QAM Order’, ‘FontSize’, 12, ‘FontWeight’, ‘bold’);
legend(‘Location’, ‘northeast’, ‘FontSize’, 11, ‘Interpreter’, ‘latex’);

% Set grid and font appearance
grid on;
set(gca, ‘FontSize’, 11, ‘FontWeight’, ‘bold’);

% Set x-axis to show QAM order directly
xticks(QAM_order); % Ensure QAM_order is numeric and sorted
xticklabels(string(QAM_order)); % Display QAM order values directly

% Set x-axis limits to start at the smallest QAM order
xlim([min(QAM_order), max(QAM_order)]); % Start from the minimum and go to the maximum QAM orderI’m working on a MATLAB project where I need to analyze and plot the relative difference in coverage area for different QAM orders and semi-angles (ϕ_1/2) for a wireless communication simulation. I’ve encountered two persistent errors:
Error using xticks (line 33)
Value must be a numeric vector whose values increase.
Error in untitled11 (line 84)
xticks(QAM_order); % Ensure QAM_order is numeric and sorted
and One more thing is the legend has multiples 15, 30, 45 as shown in the below image

Expected Outcome:
Correct x-axis ticks: The x-axis should display the QAM orders {16,32,64,128,256,512,1024}.
Unique legend entries: Each angle (ϕ1/2 =15 ∘ ,30 ∘ ,45 ∘) should appear exactly once in the legend.
Any assistance, please?
here is below my code:

clear variables;
close all;
clc;

%% Define angles and file paths
angles = [15, 30, 45]; % Define angles
angleLabels = ["15°", "30°", "45°"]; % Labels for legends
numAngles = length(angles);

% Load data from the first angle to initialize QAM_order
load([‘workspace_withBeamsteering_’, num2str(angles(1)), ‘.mat’]); % Replace with actual file path
QAM_order = unique(sort(double(QAM_order(:)))); % Ensure QAM_order is numeric, sorted, and unique
numQAMOrders = length(QAM_order); % Dynamically determine number of QAM orders

% Debug: Display QAM_order
disp(‘QAM_order:’);
disp(QAM_order);

% Initialize storage for results
coverageRelativeDiffAll = zeros(numAngles, numQAMOrders); % Initialize storage for relative differences

%% Process data for each angle
for a = 1:numAngles
angle = angles(a);

% Load data for the perfect beamsteering case
load([‘workspace_withBeamsteering_’, num2str(angle), ‘.mat’]); % Replace with actual file path
berMapPerfectCase = berMap;
coveragePerfectCase = coverage;

% Load data for the beamsteering with misalignment case
load([‘workspace_’, num2str(angle), ‘_withBeamsteering_Misalignment.mat’]); % Replace with actual file path

% Compute relative difference in coverage area for this angle
for q = 1:numQAMOrders
% Ensure correct size for coverageMap
[N_directions, N_rx, N_ry] = size(berMap{q});
coverageMap = zeros(N_rx, N_ry);

% Accumulate coverage for all directions
for i_n = 1:N_directions
currentCoverage = squeeze(berMap{q}(i_n, :, :)) < 3.8e-3;
coverageMap = coverageMap + double(currentCoverage);
end

% Compute the intersection map (points covered in all directions)
intersectionMap = (coverageMap == N_directions);

% Calculate misalignment coverage
coverageMisalignment = sum(intersectionMap(:)) * (L * W) / numel(intersectionMap);

% Compute relative difference
coverageRelativeDiffAll(a, q) = (coverageMisalignment – coveragePerfectCase(q)) / coveragePerfectCase(q);
end
end

%% Plot Relative Difference in Coverage Area for All Angles
figure;
hold on;

% Define markers and colors for each angle
markers = {‘-o’, ‘-s’, ‘-^’}; % Different markers for angles
colors = {‘b’, ‘r’, ‘g’}; % Blue, Red, Green

for a = 1:numAngles
plot(QAM_order, coverageRelativeDiffAll(a, 🙂 * 100, …
markers{a}, ‘LineWidth’, 1.5, ‘Color’, colors{a}, …
‘DisplayName’, [‘$phi_{1/2} = $ ‘, num2str(angles(a)), ‘$^circ$’]);
end

hold off;

% Add labels, title, and legend
xlabel(‘QAM Order (M)’, ‘FontSize’, 12, ‘FontWeight’, ‘bold’);
ylabel(‘Relative Difference in Coverage Area (%)’, ‘FontSize’, 12, ‘FontWeight’, ‘bold’);
title(‘Relative Difference in Coverage Area vs QAM Order’, ‘FontSize’, 12, ‘FontWeight’, ‘bold’);
legend(‘Location’, ‘northeast’, ‘FontSize’, 11, ‘Interpreter’, ‘latex’);

% Set grid and font appearance
grid on;
set(gca, ‘FontSize’, 11, ‘FontWeight’, ‘bold’);

% Set x-axis to show QAM order directly
xticks(QAM_order); % Ensure QAM_order is numeric and sorted
xticklabels(string(QAM_order)); % Display QAM order values directly

% Set x-axis limits to start at the smallest QAM order
xlim([min(QAM_order), max(QAM_order)]); % Start from the minimum and go to the maximum QAM order I’m working on a MATLAB project where I need to analyze and plot the relative difference in coverage area for different QAM orders and semi-angles (ϕ_1/2) for a wireless communication simulation. I’ve encountered two persistent errors:
Error using xticks (line 33)
Value must be a numeric vector whose values increase.
Error in untitled11 (line 84)
xticks(QAM_order); % Ensure QAM_order is numeric and sorted
and One more thing is the legend has multiples 15, 30, 45 as shown in the below image

Expected Outcome:
Correct x-axis ticks: The x-axis should display the QAM orders {16,32,64,128,256,512,1024}.
Unique legend entries: Each angle (ϕ1/2 =15 ∘ ,30 ∘ ,45 ∘) should appear exactly once in the legend.
Any assistance, please?
here is below my code:

clear variables;
close all;
clc;

%% Define angles and file paths
angles = [15, 30, 45]; % Define angles
angleLabels = ["15°", "30°", "45°"]; % Labels for legends
numAngles = length(angles);

% Load data from the first angle to initialize QAM_order
load([‘workspace_withBeamsteering_’, num2str(angles(1)), ‘.mat’]); % Replace with actual file path
QAM_order = unique(sort(double(QAM_order(:)))); % Ensure QAM_order is numeric, sorted, and unique
numQAMOrders = length(QAM_order); % Dynamically determine number of QAM orders

% Debug: Display QAM_order
disp(‘QAM_order:’);
disp(QAM_order);

% Initialize storage for results
coverageRelativeDiffAll = zeros(numAngles, numQAMOrders); % Initialize storage for relative differences

%% Process data for each angle
for a = 1:numAngles
angle = angles(a);

% Load data for the perfect beamsteering case
load([‘workspace_withBeamsteering_’, num2str(angle), ‘.mat’]); % Replace with actual file path
berMapPerfectCase = berMap;
coveragePerfectCase = coverage;

% Load data for the beamsteering with misalignment case
load([‘workspace_’, num2str(angle), ‘_withBeamsteering_Misalignment.mat’]); % Replace with actual file path

% Compute relative difference in coverage area for this angle
for q = 1:numQAMOrders
% Ensure correct size for coverageMap
[N_directions, N_rx, N_ry] = size(berMap{q});
coverageMap = zeros(N_rx, N_ry);

% Accumulate coverage for all directions
for i_n = 1:N_directions
currentCoverage = squeeze(berMap{q}(i_n, :, :)) < 3.8e-3;
coverageMap = coverageMap + double(currentCoverage);
end

% Compute the intersection map (points covered in all directions)
intersectionMap = (coverageMap == N_directions);

% Calculate misalignment coverage
coverageMisalignment = sum(intersectionMap(:)) * (L * W) / numel(intersectionMap);

% Compute relative difference
coverageRelativeDiffAll(a, q) = (coverageMisalignment – coveragePerfectCase(q)) / coveragePerfectCase(q);
end
end

%% Plot Relative Difference in Coverage Area for All Angles
figure;
hold on;

% Define markers and colors for each angle
markers = {‘-o’, ‘-s’, ‘-^’}; % Different markers for angles
colors = {‘b’, ‘r’, ‘g’}; % Blue, Red, Green

for a = 1:numAngles
plot(QAM_order, coverageRelativeDiffAll(a, 🙂 * 100, …
markers{a}, ‘LineWidth’, 1.5, ‘Color’, colors{a}, …
‘DisplayName’, [‘$phi_{1/2} = $ ‘, num2str(angles(a)), ‘$^circ$’]);
end

hold off;

% Add labels, title, and legend
xlabel(‘QAM Order (M)’, ‘FontSize’, 12, ‘FontWeight’, ‘bold’);
ylabel(‘Relative Difference in Coverage Area (%)’, ‘FontSize’, 12, ‘FontWeight’, ‘bold’);
title(‘Relative Difference in Coverage Area vs QAM Order’, ‘FontSize’, 12, ‘FontWeight’, ‘bold’);
legend(‘Location’, ‘northeast’, ‘FontSize’, 11, ‘Interpreter’, ‘latex’);

% Set grid and font appearance
grid on;
set(gca, ‘FontSize’, 11, ‘FontWeight’, ‘bold’);

% Set x-axis to show QAM order directly
xticks(QAM_order); % Ensure QAM_order is numeric and sorted
xticklabels(string(QAM_order)); % Display QAM order values directly

% Set x-axis limits to start at the smallest QAM order
xlim([min(QAM_order), max(QAM_order)]); % Start from the minimum and go to the maximum QAM order plot, xticks, legend MATLAB Answers — New Questions

​

The documentation for n4sid contains the following code snippet. I believe the line u(k-1) = -K*y(k-2) + w(k); should read u(k-2) = -K*y(k-2) + w(k);.

N = 1000;
K = 0.5;
rng(‘default’);
w = randn(N,1);
z = zeros(N,1);
u = zeros(N,1);
y = zeros(N,1);
e = randn(N,1);
v = filter([1 0.5],[1 1.5 0.7],e);
for k = 3:N
u(k-1) = -K*y(k-2) + w(k);
u(k-1) = -K*y(k-1) + w(k);
z(k) = 1.5*z(k-1) – 0.7*z(k-2) + u(k-1) + 0.5*u(k-2);
y(k) = z(k) + 0.8*v(k);
end
dat = iddata(y, u, 1);The documentation for n4sid contains the following code snippet. I believe the line u(k-1) = -K*y(k-2) + w(k); should read u(k-2) = -K*y(k-2) + w(k);.

N = 1000;
K = 0.5;
rng(‘default’);
w = randn(N,1);
z = zeros(N,1);
u = zeros(N,1);
y = zeros(N,1);
e = randn(N,1);
v = filter([1 0.5],[1 1.5 0.7],e);
for k = 3:N
u(k-1) = -K*y(k-2) + w(k);
u(k-1) = -K*y(k-1) + w(k);
z(k) = 1.5*z(k-1) – 0.7*z(k-2) + u(k-1) + 0.5*u(k-2);
y(k) = z(k) + 0.8*v(k);
end
dat = iddata(y, u, 1); The documentation for n4sid contains the following code snippet. I believe the line u(k-1) = -K*y(k-2) + w(k); should read u(k-2) = -K*y(k-2) + w(k);.

N = 1000;
K = 0.5;
rng(‘default’);
w = randn(N,1);
z = zeros(N,1);
u = zeros(N,1);
y = zeros(N,1);
e = randn(N,1);
v = filter([1 0.5],[1 1.5 0.7],e);
for k = 3:N
u(k-1) = -K*y(k-2) + w(k);
u(k-1) = -K*y(k-1) + w(k);
z(k) = 1.5*z(k-1) – 0.7*z(k-2) + u(k-1) + 0.5*u(k-2);
y(k) = z(k) + 0.8*v(k);
end
dat = iddata(y, u, 1); documentation MATLAB Answers — New Questions

​

Hello, I am trying to figure out how to use a cplex function and connect it to MATLAB. The code that I have in MATLAB calls a cplexmiqp function.
I am going to use the MIQPS_MATPOWER Mixed Integer Quadratic Program Solver for MATPOWER, from this page https://matpower.org/docs/ref/matpower6.0/miqps_cplex.html.
Now my question is, how would I connect IBM cplex to MATLAB to call such a fucntion. I am using a MAC and MATLAB R2022a. What CPLEX version is compatible?Hello, I am trying to figure out how to use a cplex function and connect it to MATLAB. The code that I have in MATLAB calls a cplexmiqp function.
I am going to use the MIQPS_MATPOWER Mixed Integer Quadratic Program Solver for MATPOWER, from this page https://matpower.org/docs/ref/matpower6.0/miqps_cplex.html.
Now my question is, how would I connect IBM cplex to MATLAB to call such a fucntion. I am using a MAC and MATLAB R2022a. What CPLEX version is compatible? Hello, I am trying to figure out how to use a cplex function and connect it to MATLAB. The code that I have in MATLAB calls a cplexmiqp function.
I am going to use the MIQPS_MATPOWER Mixed Integer Quadratic Program Solver for MATPOWER, from this page https://matpower.org/docs/ref/matpower6.0/miqps_cplex.html.
Now my question is, how would I connect IBM cplex to MATLAB to call such a fucntion. I am using a MAC and MATLAB R2022a. What CPLEX version is compatible? ibm cplex, matlab, optimization MATLAB Answers — New Questions

​

how can dameged image and restored image by harmonic functionhow can dameged image and restored image by harmonic function how can dameged image and restored image by harmonic function harmonic function MATLAB Answers — New Questions

​

Dear colleagues,
I need to perform a regression. The input data is shown in Figure 1 and is also attached as an MS Excel file.
Figure 1 (Figures are shown in the end of the letter.
The equation is z = a*x^m/(y+c)^n.
The parameters of the fit are shown in Figure 2 and are also attached.
Figure 2

The output data is shown in Figure 3.
Figure 3
The individual differences in percentage are shown in Figure 4.
Figure 4
I know that by using a weighted matrix, it is possible to decrease some differences (priority zones) at the expense of increasing others. However, in this case, my goal is to estimate the minimal possible error (one border value) for all of the values. For example, in the first fit, the maximum difference is 93%. It is easy to decrease it by applying individual weight coefficients, but of course, somewhere, the difference will increase. Therefore, the question is: what method can be used to find the minimal possible difference? If this for example is 18%, it means that everywhere the difference will be smaller, and it is likely that there is no 0 % anymore. Example by hand (not real) is given in Figure 5.
Figure 5
And if we try to decrease the difference at the point with an 18% difference, this will lead to an increase in the difference somewhere above 18%. Also, if in the first fit I have a 0% or 1% difference at a given point, in the second fit, the difference at this point is likely to be close (but smaller) to 18%. And one more thing – if there is a way for doing the whole procedure, is it possible to use curve fitter app or need to use a code?Dear colleagues,
I need to perform a regression. The input data is shown in Figure 1 and is also attached as an MS Excel file.
Figure 1 (Figures are shown in the end of the letter.
The equation is z = a*x^m/(y+c)^n.
The parameters of the fit are shown in Figure 2 and are also attached.
Figure 2

The output data is shown in Figure 3.
Figure 3
The individual differences in percentage are shown in Figure 4.
Figure 4
I know that by using a weighted matrix, it is possible to decrease some differences (priority zones) at the expense of increasing others. However, in this case, my goal is to estimate the minimal possible error (one border value) for all of the values. For example, in the first fit, the maximum difference is 93%. It is easy to decrease it by applying individual weight coefficients, but of course, somewhere, the difference will increase. Therefore, the question is: what method can be used to find the minimal possible difference? If this for example is 18%, it means that everywhere the difference will be smaller, and it is likely that there is no 0 % anymore. Example by hand (not real) is given in Figure 5.
Figure 5
And if we try to decrease the difference at the point with an 18% difference, this will lead to an increase in the difference somewhere above 18%. Also, if in the first fit I have a 0% or 1% difference at a given point, in the second fit, the difference at this point is likely to be close (but smaller) to 18%. And one more thing – if there is a way for doing the whole procedure, is it possible to use curve fitter app or need to use a code? Dear colleagues,
I need to perform a regression. The input data is shown in Figure 1 and is also attached as an MS Excel file.
Figure 1 (Figures are shown in the end of the letter.
The equation is z = a*x^m/(y+c)^n.
The parameters of the fit are shown in Figure 2 and are also attached.
Figure 2

The output data is shown in Figure 3.
Figure 3
The individual differences in percentage are shown in Figure 4.
Figure 4
I know that by using a weighted matrix, it is possible to decrease some differences (priority zones) at the expense of increasing others. However, in this case, my goal is to estimate the minimal possible error (one border value) for all of the values. For example, in the first fit, the maximum difference is 93%. It is easy to decrease it by applying individual weight coefficients, but of course, somewhere, the difference will increase. Therefore, the question is: what method can be used to find the minimal possible difference? If this for example is 18%, it means that everywhere the difference will be smaller, and it is likely that there is no 0 % anymore. Example by hand (not real) is given in Figure 5.
Figure 5
And if we try to decrease the difference at the point with an 18% difference, this will lead to an increase in the difference somewhere above 18%. Also, if in the first fit I have a 0% or 1% difference at a given point, in the second fit, the difference at this point is likely to be close (but smaller) to 18%. And one more thing – if there is a way for doing the whole procedure, is it possible to use curve fitter app or need to use a code? curve fitter toolbox, weighted regression MATLAB Answers — New Questions

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