Hi to everyone!
How to implement in the Simbiology interface so that the estimated parameters in a fit satisfy a given condition? For example, estimate by a fit the values ​​of the kinetic constants k1 and k-1 for a serie of experimental data, which satisfy the value of the equilibrium constant Keq (Keq= k1/k-1) within a given interval, such as K= 10±2.
Thank you in advance!Hi to everyone!
How to implement in the Simbiology interface so that the estimated parameters in a fit satisfy a given condition? For example, estimate by a fit the values ​​of the kinetic constants k1 and k-1 for a serie of experimental data, which satisfy the value of the equilibrium constant Keq (Keq= k1/k-1) within a given interval, such as K= 10±2.
Thank you in advance! Hi to everyone!
How to implement in the Simbiology interface so that the estimated parameters in a fit satisfy a given condition? For example, estimate by a fit the values ​​of the kinetic constants k1 and k-1 for a serie of experimental data, which satisfy the value of the equilibrium constant Keq (Keq= k1/k-1) within a given interval, such as K= 10±2.
Thank you in advance! simbiology MATLAB Answers — New Questions

​

% This code, after demodulation, produces a sequence of 4 values, although the number of values ​​should be equal to the modulation factor M. What could be the problem?

clc
close all
clear all
M = 16;
L=10000;
T=10;
W=43; %SNR

bits1 = randi([0 M-1], 1, L);
bits2 = randi([0 3], 1, L/T);
mod1 = qammod(bits1, M,’UnitAveragePower’, true);
% scatterplot(mod1)
mod2 = qammod(bits2, 4, ‘UnitAveragePower’,true);
% scatterplot(mod2)
for i = 1:length(bits2)
sym(i,:)=[mod1((i-1)*T+1:i*T) mod2(i)];
end
f=0;
for k=1:L/T
for j=1:T+1
f=f+1;
symbols(f)=sym(k,j);
end
end
% scatterplot(symbols)

% symbols = awgn(symbols,W,"measured");
pnoise = comm.PhaseNoise(‘Level’,[-70 -104 -110],’FrequencyOffset’,[1e4 1e5 2e5], ‘SampleRate’, 28e6);
symbols2 = pnoise([zeros(1e5,1);symbols’]);
% symbols2 = pnoise(symbols);
fc = 1e6; % Carrier frequency in Hz
fs = 28e6; % Sample rate in Hz.
phNzLevel = [-70 -104 -110]; % in dBc/Hz
phNzFreqOff = [1e4 1e5 2e5]; % in Hz
Nspf = 6e6; % Number of Samples per frame
freqSpan = 400e3; % in Hz, for spectrum computation
sinewave = dsp.SineWave( …
Amplitude=1, …
Frequency=fc, …
SampleRate=fs, …
SamplesPerFrame=Nspf, …
ComplexOutput=true);
pnoise = comm.PhaseNoise( …
Level=phNzLevel, …
FrequencyOffset=phNzFreqOff, …
SampleRate=fs);
sascopeRBW100 = spectrumAnalyzer( …
SampleRate=fs, …
Method="welch", …
FrequencySpan="Span and center frequency", …
CenterFrequency=fc, …
Span=freqSpan, …
RBWSource="Property", …
RBW=100, …
SpectrumType="Power density", …
SpectralAverages=10, …
SpectrumUnits="dBW", …
YLimits=[-150 10], …
Title="Resolution Bandwidth 100 Hz", …
ChannelNames={‘signal’,’signal with phase noise’}, …
Position=[79 147 605 374]);
x = sinewave();
y = pnoise(x);
sascopeRBW100(x,y)
symbols2 = symbols2(1e5+1:end);

prim(1,:)=symbols2(1:T);
for i = 1:length(bits2)-1
prim(i+1,:)=symbols2(i*T+i+1:i*T+i+T);
end
for b = 1:length(mod2)
qam4(b)=symbols2((T+1)*b);
end
h=0;
for u=1:L/T
for l=1:T
h=h+1;
priem(h)=prim(u,l);
end
end
for g = 1:L/T
phase_error(g) = angle(qam4(g) / mod2(g));
compensated4(g) = qam4(g) .* exp(-1i * phase_error(g));
end
for v = 1:L/T
phase_errorM((v-1)*T+1:v*T)=phase_error(v);
end
for f = 1:L
compensatedM(f) = priem(f) .* exp(-1i*phase_errorM(f));
end
demod=qamdemod(compensatedM, M, ‘bin’,’OutputType’,’bit’);
% scatterplot(qam4)
% scatterplot(symbols2)
% scatterplot(compensatedM)
[number,ratio]=biterr(bits1,demod);
evm = lteEVM(demod,bits1);
figure(‘Position’,[200 200 1080 540])
subplot(1,2,1)
scatter(real(symbols2),imag(symbols2),300,".")
subplot(1,2,2)
scatter(real(compensatedM),imag(compensatedM),300,".")% This code, after demodulation, produces a sequence of 4 values, although the number of values ​​should be equal to the modulation factor M. What could be the problem?

clc
close all
clear all
M = 16;
L=10000;
T=10;
W=43; %SNR

bits1 = randi([0 M-1], 1, L);
bits2 = randi([0 3], 1, L/T);
mod1 = qammod(bits1, M,’UnitAveragePower’, true);
% scatterplot(mod1)
mod2 = qammod(bits2, 4, ‘UnitAveragePower’,true);
% scatterplot(mod2)
for i = 1:length(bits2)
sym(i,:)=[mod1((i-1)*T+1:i*T) mod2(i)];
end
f=0;
for k=1:L/T
for j=1:T+1
f=f+1;
symbols(f)=sym(k,j);
end
end
% scatterplot(symbols)

% symbols = awgn(symbols,W,"measured");
pnoise = comm.PhaseNoise(‘Level’,[-70 -104 -110],’FrequencyOffset’,[1e4 1e5 2e5], ‘SampleRate’, 28e6);
symbols2 = pnoise([zeros(1e5,1);symbols’]);
% symbols2 = pnoise(symbols);
fc = 1e6; % Carrier frequency in Hz
fs = 28e6; % Sample rate in Hz.
phNzLevel = [-70 -104 -110]; % in dBc/Hz
phNzFreqOff = [1e4 1e5 2e5]; % in Hz
Nspf = 6e6; % Number of Samples per frame
freqSpan = 400e3; % in Hz, for spectrum computation
sinewave = dsp.SineWave( …
Amplitude=1, …
Frequency=fc, …
SampleRate=fs, …
SamplesPerFrame=Nspf, …
ComplexOutput=true);
pnoise = comm.PhaseNoise( …
Level=phNzLevel, …
FrequencyOffset=phNzFreqOff, …
SampleRate=fs);
sascopeRBW100 = spectrumAnalyzer( …
SampleRate=fs, …
Method="welch", …
FrequencySpan="Span and center frequency", …
CenterFrequency=fc, …
Span=freqSpan, …
RBWSource="Property", …
RBW=100, …
SpectrumType="Power density", …
SpectralAverages=10, …
SpectrumUnits="dBW", …
YLimits=[-150 10], …
Title="Resolution Bandwidth 100 Hz", …
ChannelNames={‘signal’,’signal with phase noise’}, …
Position=[79 147 605 374]);
x = sinewave();
y = pnoise(x);
sascopeRBW100(x,y)
symbols2 = symbols2(1e5+1:end);

prim(1,:)=symbols2(1:T);
for i = 1:length(bits2)-1
prim(i+1,:)=symbols2(i*T+i+1:i*T+i+T);
end
for b = 1:length(mod2)
qam4(b)=symbols2((T+1)*b);
end
h=0;
for u=1:L/T
for l=1:T
h=h+1;
priem(h)=prim(u,l);
end
end
for g = 1:L/T
phase_error(g) = angle(qam4(g) / mod2(g));
compensated4(g) = qam4(g) .* exp(-1i * phase_error(g));
end
for v = 1:L/T
phase_errorM((v-1)*T+1:v*T)=phase_error(v);
end
for f = 1:L
compensatedM(f) = priem(f) .* exp(-1i*phase_errorM(f));
end
demod=qamdemod(compensatedM, M, ‘bin’,’OutputType’,’bit’);
% scatterplot(qam4)
% scatterplot(symbols2)
% scatterplot(compensatedM)
[number,ratio]=biterr(bits1,demod);
evm = lteEVM(demod,bits1);
figure(‘Position’,[200 200 1080 540])
subplot(1,2,1)
scatter(real(symbols2),imag(symbols2),300,".")
subplot(1,2,2)
scatter(real(compensatedM),imag(compensatedM),300,".") % This code, after demodulation, produces a sequence of 4 values, although the number of values ​​should be equal to the modulation factor M. What could be the problem?

clc
close all
clear all
M = 16;
L=10000;
T=10;
W=43; %SNR

bits1 = randi([0 M-1], 1, L);
bits2 = randi([0 3], 1, L/T);
mod1 = qammod(bits1, M,’UnitAveragePower’, true);
% scatterplot(mod1)
mod2 = qammod(bits2, 4, ‘UnitAveragePower’,true);
% scatterplot(mod2)
for i = 1:length(bits2)
sym(i,:)=[mod1((i-1)*T+1:i*T) mod2(i)];
end
f=0;
for k=1:L/T
for j=1:T+1
f=f+1;
symbols(f)=sym(k,j);
end
end
% scatterplot(symbols)

% symbols = awgn(symbols,W,"measured");
pnoise = comm.PhaseNoise(‘Level’,[-70 -104 -110],’FrequencyOffset’,[1e4 1e5 2e5], ‘SampleRate’, 28e6);
symbols2 = pnoise([zeros(1e5,1);symbols’]);
% symbols2 = pnoise(symbols);
fc = 1e6; % Carrier frequency in Hz
fs = 28e6; % Sample rate in Hz.
phNzLevel = [-70 -104 -110]; % in dBc/Hz
phNzFreqOff = [1e4 1e5 2e5]; % in Hz
Nspf = 6e6; % Number of Samples per frame
freqSpan = 400e3; % in Hz, for spectrum computation
sinewave = dsp.SineWave( …
Amplitude=1, …
Frequency=fc, …
SampleRate=fs, …
SamplesPerFrame=Nspf, …
ComplexOutput=true);
pnoise = comm.PhaseNoise( …
Level=phNzLevel, …
FrequencyOffset=phNzFreqOff, …
SampleRate=fs);
sascopeRBW100 = spectrumAnalyzer( …
SampleRate=fs, …
Method="welch", …
FrequencySpan="Span and center frequency", …
CenterFrequency=fc, …
Span=freqSpan, …
RBWSource="Property", …
RBW=100, …
SpectrumType="Power density", …
SpectralAverages=10, …
SpectrumUnits="dBW", …
YLimits=[-150 10], …
Title="Resolution Bandwidth 100 Hz", …
ChannelNames={‘signal’,’signal with phase noise’}, …
Position=[79 147 605 374]);
x = sinewave();
y = pnoise(x);
sascopeRBW100(x,y)
symbols2 = symbols2(1e5+1:end);

prim(1,:)=symbols2(1:T);
for i = 1:length(bits2)-1
prim(i+1,:)=symbols2(i*T+i+1:i*T+i+T);
end
for b = 1:length(mod2)
qam4(b)=symbols2((T+1)*b);
end
h=0;
for u=1:L/T
for l=1:T
h=h+1;
priem(h)=prim(u,l);
end
end
for g = 1:L/T
phase_error(g) = angle(qam4(g) / mod2(g));
compensated4(g) = qam4(g) .* exp(-1i * phase_error(g));
end
for v = 1:L/T
phase_errorM((v-1)*T+1:v*T)=phase_error(v);
end
for f = 1:L
compensatedM(f) = priem(f) .* exp(-1i*phase_errorM(f));
end
demod=qamdemod(compensatedM, M, ‘bin’,’OutputType’,’bit’);
% scatterplot(qam4)
% scatterplot(symbols2)
% scatterplot(compensatedM)
[number,ratio]=biterr(bits1,demod);
evm = lteEVM(demod,bits1);
figure(‘Position’,[200 200 1080 540])
subplot(1,2,1)
scatter(real(symbols2),imag(symbols2),300,".")
subplot(1,2,2)
scatter(real(compensatedM),imag(compensatedM),300,".") qam16, demodulation, ber MATLAB Answers — New Questions

​

Ιn continuation of the study with which I have been fascinated Numerical Simulation of a Damped, Driven Nonlinear Wave System with Spatially Extended Initial Conditions
Now we examine the role of damping in the structure of the branches of equilibrium points. In this study, we consider localized initial conditions of the form:
where is the distance between the nodes, and is the amplitude. We assume zero initial velocity

We also note that if we consider an infinite chain, the initial conditions $(1)$ satisfy the Dirichlet boundary conditions only asymptotically. However, since the smallest half-length of the chain has been set to $ L/2 = 100$, the error is of the order of $ 10^{-44} $, which does not affect numerical computations.

The figure shows the dynamics of the initial condition (1) for parameters: and . In the first image of Figure, we observe the profile of the initial condition.

The subsequent images show the dynamics of the system for different values of the damping force . For , the convergence takes place at the equilibrium point which is the basic state.

I want to create the red and the blue plots

Now I have tried but as you can see the result are different. What should I change?
% Parameters
L = 200; % Length of the system
K = 99; % Number of spatial points
omega_d = 1; % Characteristic frequency
beta = 1; % Nonlinearity parameter
delta_values = [0.1, 0.05, 0.01]; % Damping coefficients

% Spatial grid
h = L / (K + 1);
n = linspace(-L/2, L/2, K+1); % Spatial points
N = length(n);
omegaDScaled = h * omega_d;

% Time parameters
dt = 1; % Time step
tmax = 3000; % Maximum time
tspan = 0:dt:tmax; % Time vector

% Values of amplitude ‘a’ to iterate over
a_values = [2]; % Initial amplitude for the initial condition plot

% Differential equation solver function
function dYdt = odefun(~, Y, N, h, omegaDScaled, deltaScaled, beta)
U = Y(1:N);
Udot = Y(N+1:end);
Uddot = zeros(size(U));
% Laplacian (discrete second derivative)
for k = 2:N-1
Uddot(k) = (U(k+1) – 2 * U(k) + U(k-1)) ;
end
% System of equations
dUdt = Udot;
dUdotdt = Uddot – deltaScaled * Udot + omegaDScaled^2 * (U – beta * U.^3);
% Pack derivatives
dYdt = [dUdt; dUdotdt];
end

% Create a figure for subplots
figure;

% Loop through each value of ‘delta’ and generate the plot
for i = 1:length(delta_values)
delta = delta_values(i);
deltaScaled = h * delta;
a = a_values(1);
% Initial conditions
U0 = a * sech(-L/2 + n*h); % Initial displacement
U0(1) = 0; % Boundary condition at n = 0
U0(end) = 0; % Boundary condition at n = K+1
Udot0 = zeros(size(U0)); % Initial velocity
% Pack initial conditions
Y0 = [U0, Udot0];
% Solve ODE
opts = odeset(‘RelTol’, 1e-5, ‘AbsTol’, 1e-6);
[t, Y] = ode45(@(t, Y) odefun(t, Y, N, h, omegaDScaled, deltaScaled, beta), tspan, Y0, opts);
% Extract solutions
U = Y(:, 1:N);
Udot = Y(:, N+1:end);
% Plot final displacement profile
subplot(2, 2, i+1);
plot(n, U(end,:), ‘b.-‘, ‘LineWidth’, 1.5, ‘MarkerSize’, 10); % Line and marker plot
xlabel(‘$x_n$’, ‘Interpreter’, ‘latex’);
ylabel(‘$U_n$’, ‘Interpreter’, ‘latex’);
title([‘$t=3000$, $delta=’, num2str(delta), ‘$’], ‘Interpreter’, ‘latex’);
set(gca, ‘FontSize’, 12, ‘FontName’, ‘Times’);
xlim([-L/2 L/2]);
ylim([-2 2]);
grid on;
end

% Initial plot
subplot(2, 2, 1);
a_init = 2; % Example initial amplitude for the initial condition plot
U0_init = a_init * sech(-L/2 + n*h); % Initial displacement
U0_init(1) = 0; % Boundary condition at n = 0
U0_init(end) = 0; % Boundary condition at n = K+1
plot(n, U0_init, ‘r.-‘, ‘LineWidth’, 1.5, ‘MarkerSize’, 10); % Line and marker plot
xlabel(‘$x_n$’, ‘Interpreter’, ‘latex’);
ylabel(‘$U_n$’, ‘Interpreter’, ‘latex’);
title(‘$t=0$’, ‘Interpreter’, ‘latex’);
set(gca, ‘FontSize’, 12, ‘FontName’, ‘Times’);
xlim([-L/2 L/2]);
ylim([-3 3]);
grid on;

% Adjust layout
set(gcf, ‘Position’, [100, 100, 1200, 900]); % Adjust figure size as neededΙn continuation of the study with which I have been fascinated Numerical Simulation of a Damped, Driven Nonlinear Wave System with Spatially Extended Initial Conditions
Now we examine the role of damping in the structure of the branches of equilibrium points. In this study, we consider localized initial conditions of the form:
where is the distance between the nodes, and is the amplitude. We assume zero initial velocity

We also note that if we consider an infinite chain, the initial conditions $(1)$ satisfy the Dirichlet boundary conditions only asymptotically. However, since the smallest half-length of the chain has been set to $ L/2 = 100$, the error is of the order of $ 10^{-44} $, which does not affect numerical computations.

The figure shows the dynamics of the initial condition (1) for parameters: and . In the first image of Figure, we observe the profile of the initial condition.

The subsequent images show the dynamics of the system for different values of the damping force . For , the convergence takes place at the equilibrium point which is the basic state.

I want to create the red and the blue plots

Now I have tried but as you can see the result are different. What should I change?
% Parameters
L = 200; % Length of the system
K = 99; % Number of spatial points
omega_d = 1; % Characteristic frequency
beta = 1; % Nonlinearity parameter
delta_values = [0.1, 0.05, 0.01]; % Damping coefficients

% Spatial grid
h = L / (K + 1);
n = linspace(-L/2, L/2, K+1); % Spatial points
N = length(n);
omegaDScaled = h * omega_d;

% Time parameters
dt = 1; % Time step
tmax = 3000; % Maximum time
tspan = 0:dt:tmax; % Time vector

% Values of amplitude ‘a’ to iterate over
a_values = [2]; % Initial amplitude for the initial condition plot

% Differential equation solver function
function dYdt = odefun(~, Y, N, h, omegaDScaled, deltaScaled, beta)
U = Y(1:N);
Udot = Y(N+1:end);
Uddot = zeros(size(U));
% Laplacian (discrete second derivative)
for k = 2:N-1
Uddot(k) = (U(k+1) – 2 * U(k) + U(k-1)) ;
end
% System of equations
dUdt = Udot;
dUdotdt = Uddot – deltaScaled * Udot + omegaDScaled^2 * (U – beta * U.^3);
% Pack derivatives
dYdt = [dUdt; dUdotdt];
end

% Create a figure for subplots
figure;

% Loop through each value of ‘delta’ and generate the plot
for i = 1:length(delta_values)
delta = delta_values(i);
deltaScaled = h * delta;
a = a_values(1);
% Initial conditions
U0 = a * sech(-L/2 + n*h); % Initial displacement
U0(1) = 0; % Boundary condition at n = 0
U0(end) = 0; % Boundary condition at n = K+1
Udot0 = zeros(size(U0)); % Initial velocity
% Pack initial conditions
Y0 = [U0, Udot0];
% Solve ODE
opts = odeset(‘RelTol’, 1e-5, ‘AbsTol’, 1e-6);
[t, Y] = ode45(@(t, Y) odefun(t, Y, N, h, omegaDScaled, deltaScaled, beta), tspan, Y0, opts);
% Extract solutions
U = Y(:, 1:N);
Udot = Y(:, N+1:end);
% Plot final displacement profile
subplot(2, 2, i+1);
plot(n, U(end,:), ‘b.-‘, ‘LineWidth’, 1.5, ‘MarkerSize’, 10); % Line and marker plot
xlabel(‘$x_n$’, ‘Interpreter’, ‘latex’);
ylabel(‘$U_n$’, ‘Interpreter’, ‘latex’);
title([‘$t=3000$, $delta=’, num2str(delta), ‘$’], ‘Interpreter’, ‘latex’);
set(gca, ‘FontSize’, 12, ‘FontName’, ‘Times’);
xlim([-L/2 L/2]);
ylim([-2 2]);
grid on;
end

% Initial plot
subplot(2, 2, 1);
a_init = 2; % Example initial amplitude for the initial condition plot
U0_init = a_init * sech(-L/2 + n*h); % Initial displacement
U0_init(1) = 0; % Boundary condition at n = 0
U0_init(end) = 0; % Boundary condition at n = K+1
plot(n, U0_init, ‘r.-‘, ‘LineWidth’, 1.5, ‘MarkerSize’, 10); % Line and marker plot
xlabel(‘$x_n$’, ‘Interpreter’, ‘latex’);
ylabel(‘$U_n$’, ‘Interpreter’, ‘latex’);
title(‘$t=0$’, ‘Interpreter’, ‘latex’);
set(gca, ‘FontSize’, 12, ‘FontName’, ‘Times’);
xlim([-L/2 L/2]);
ylim([-3 3]);
grid on;

% Adjust layout
set(gcf, ‘Position’, [100, 100, 1200, 900]); % Adjust figure size as needed Ιn continuation of the study with which I have been fascinated Numerical Simulation of a Damped, Driven Nonlinear Wave System with Spatially Extended Initial Conditions
Now we examine the role of damping in the structure of the branches of equilibrium points. In this study, we consider localized initial conditions of the form:
where is the distance between the nodes, and is the amplitude. We assume zero initial velocity

We also note that if we consider an infinite chain, the initial conditions $(1)$ satisfy the Dirichlet boundary conditions only asymptotically. However, since the smallest half-length of the chain has been set to $ L/2 = 100$, the error is of the order of $ 10^{-44} $, which does not affect numerical computations.

The figure shows the dynamics of the initial condition (1) for parameters: and . In the first image of Figure, we observe the profile of the initial condition.

The subsequent images show the dynamics of the system for different values of the damping force . For , the convergence takes place at the equilibrium point which is the basic state.

I want to create the red and the blue plots

Now I have tried but as you can see the result are different. What should I change?
% Parameters
L = 200; % Length of the system
K = 99; % Number of spatial points
omega_d = 1; % Characteristic frequency
beta = 1; % Nonlinearity parameter
delta_values = [0.1, 0.05, 0.01]; % Damping coefficients

% Spatial grid
h = L / (K + 1);
n = linspace(-L/2, L/2, K+1); % Spatial points
N = length(n);
omegaDScaled = h * omega_d;

% Time parameters
dt = 1; % Time step
tmax = 3000; % Maximum time
tspan = 0:dt:tmax; % Time vector

% Values of amplitude ‘a’ to iterate over
a_values = [2]; % Initial amplitude for the initial condition plot

% Differential equation solver function
function dYdt = odefun(~, Y, N, h, omegaDScaled, deltaScaled, beta)
U = Y(1:N);
Udot = Y(N+1:end);
Uddot = zeros(size(U));
% Laplacian (discrete second derivative)
for k = 2:N-1
Uddot(k) = (U(k+1) – 2 * U(k) + U(k-1)) ;
end
% System of equations
dUdt = Udot;
dUdotdt = Uddot – deltaScaled * Udot + omegaDScaled^2 * (U – beta * U.^3);
% Pack derivatives
dYdt = [dUdt; dUdotdt];
end

% Create a figure for subplots
figure;

% Loop through each value of ‘delta’ and generate the plot
for i = 1:length(delta_values)
delta = delta_values(i);
deltaScaled = h * delta;
a = a_values(1);
% Initial conditions
U0 = a * sech(-L/2 + n*h); % Initial displacement
U0(1) = 0; % Boundary condition at n = 0
U0(end) = 0; % Boundary condition at n = K+1
Udot0 = zeros(size(U0)); % Initial velocity
% Pack initial conditions
Y0 = [U0, Udot0];
% Solve ODE
opts = odeset(‘RelTol’, 1e-5, ‘AbsTol’, 1e-6);
[t, Y] = ode45(@(t, Y) odefun(t, Y, N, h, omegaDScaled, deltaScaled, beta), tspan, Y0, opts);
% Extract solutions
U = Y(:, 1:N);
Udot = Y(:, N+1:end);
% Plot final displacement profile
subplot(2, 2, i+1);
plot(n, U(end,:), ‘b.-‘, ‘LineWidth’, 1.5, ‘MarkerSize’, 10); % Line and marker plot
xlabel(‘$x_n$’, ‘Interpreter’, ‘latex’);
ylabel(‘$U_n$’, ‘Interpreter’, ‘latex’);
title([‘$t=3000$, $delta=’, num2str(delta), ‘$’], ‘Interpreter’, ‘latex’);
set(gca, ‘FontSize’, 12, ‘FontName’, ‘Times’);
xlim([-L/2 L/2]);
ylim([-2 2]);
grid on;
end

% Initial plot
subplot(2, 2, 1);
a_init = 2; % Example initial amplitude for the initial condition plot
U0_init = a_init * sech(-L/2 + n*h); % Initial displacement
U0_init(1) = 0; % Boundary condition at n = 0
U0_init(end) = 0; % Boundary condition at n = K+1
plot(n, U0_init, ‘r.-‘, ‘LineWidth’, 1.5, ‘MarkerSize’, 10); % Line and marker plot
xlabel(‘$x_n$’, ‘Interpreter’, ‘latex’);
ylabel(‘$U_n$’, ‘Interpreter’, ‘latex’);
title(‘$t=0$’, ‘Interpreter’, ‘latex’);
set(gca, ‘FontSize’, 12, ‘FontName’, ‘Times’);
xlim([-L/2 L/2]);
ylim([-3 3]);
grid on;

% Adjust layout
set(gcf, ‘Position’, [100, 100, 1200, 900]); % Adjust figure size as needed differential equations, plotting, subplot, plot, equation, mathematics MATLAB Answers — New Questions

​

*Problem Description:*

Given a large project with interfaces defined in the form:

namespace ns1
{
class MyClass
{
public:
enum myEnum : int8
{
FOO = 0,
BAR = 1,
FOOBAR = 2
};

myEnum m_myEnum;
}
}

namespace ns2
{
class MyOtherClass
{
enum myOtherEnum : int8
{
FOO = 0,
BAR = 1,
FOOBAR = 2
};

myOtherEnum m_myOtherEnum;
}
}

Within C++, the value of the first enum could be accessed using ns1::MyClass::myEnum. In order to write on variables using these interfaces from a Stateflow generated C-File, I only came up with one solution so far: to have the Code Generator redefine both interfaces in C-Style, based on the values entered within Stateflow:

typedef enum
{
NS2_MYCLASS_MY_ENUM_FOO = 0,
NS2_MYCLASS_MY_ENUM_BAR = 1,
NS2_MYCLASS_MY_ENUM_FOOBAR = 2,
} MY_ENUM

typedef enum
{
NS2_MYOTHERCLASS_MY_OTHER_ENUM_FOO = 0,
NS2_MYOTHERCLASS_MY_OTHER_ENUM_BAR = 1,
NS2_MYOTHERCLASS_MY_OTHER_ENUM_FOOBAR = 2,
} MY_OTHER_ENUM

To get these values back on variables, defined in the form of the original enums, there have to be static casts implemented like e.g.

instanceOfMyClass.m_myEnum = static_cast<ns1::MyClass::myEnum>(MY_ENUM)
instanceOfMyOtherClass.m_myOtherEnum = static_cast<ns2::MyOtherClass::myOtherEnum>(MY_OTHER_ENUM)

The disadvantages are:

* redefinition is extra work, prone to errors and a lot of work to keep in sync
* static casting is dangerous, especially, when e.g. the order of the values within the enumeration changes
* the principle of single source of truth is violated which leads to these disadvantages

*Question:*

* Is there a way to directly use the original, C++-Style enums without recreating them and having all the stated disadvantages?
* I’m thinking about a solution where I can enter the names and paths of the original interfaces directly within StateFlow and generating C++ code that is therefore already integrated – without any wrapping and extra, error prone work.*Problem Description:*

Given a large project with interfaces defined in the form:

namespace ns1
{
class MyClass
{
public:
enum myEnum : int8
{
FOO = 0,
BAR = 1,
FOOBAR = 2
};

myEnum m_myEnum;
}
}

namespace ns2
{
class MyOtherClass
{
enum myOtherEnum : int8
{
FOO = 0,
BAR = 1,
FOOBAR = 2
};

myOtherEnum m_myOtherEnum;
}
}

Within C++, the value of the first enum could be accessed using ns1::MyClass::myEnum. In order to write on variables using these interfaces from a Stateflow generated C-File, I only came up with one solution so far: to have the Code Generator redefine both interfaces in C-Style, based on the values entered within Stateflow:

typedef enum
{
NS2_MYCLASS_MY_ENUM_FOO = 0,
NS2_MYCLASS_MY_ENUM_BAR = 1,
NS2_MYCLASS_MY_ENUM_FOOBAR = 2,
} MY_ENUM

typedef enum
{
NS2_MYOTHERCLASS_MY_OTHER_ENUM_FOO = 0,
NS2_MYOTHERCLASS_MY_OTHER_ENUM_BAR = 1,
NS2_MYOTHERCLASS_MY_OTHER_ENUM_FOOBAR = 2,
} MY_OTHER_ENUM

To get these values back on variables, defined in the form of the original enums, there have to be static casts implemented like e.g.

instanceOfMyClass.m_myEnum = static_cast<ns1::MyClass::myEnum>(MY_ENUM)
instanceOfMyOtherClass.m_myOtherEnum = static_cast<ns2::MyOtherClass::myOtherEnum>(MY_OTHER_ENUM)

The disadvantages are:

* redefinition is extra work, prone to errors and a lot of work to keep in sync
* static casting is dangerous, especially, when e.g. the order of the values within the enumeration changes
* the principle of single source of truth is violated which leads to these disadvantages

*Question:*

* Is there a way to directly use the original, C++-Style enums without recreating them and having all the stated disadvantages?
* I’m thinking about a solution where I can enter the names and paths of the original interfaces directly within StateFlow and generating C++ code that is therefore already integrated – without any wrapping and extra, error prone work. *Problem Description:*

Given a large project with interfaces defined in the form:

namespace ns1
{
class MyClass
{
public:
enum myEnum : int8
{
FOO = 0,
BAR = 1,
FOOBAR = 2
};

myEnum m_myEnum;
}
}

namespace ns2
{
class MyOtherClass
{
enum myOtherEnum : int8
{
FOO = 0,
BAR = 1,
FOOBAR = 2
};

myOtherEnum m_myOtherEnum;
}
}

Within C++, the value of the first enum could be accessed using ns1::MyClass::myEnum. In order to write on variables using these interfaces from a Stateflow generated C-File, I only came up with one solution so far: to have the Code Generator redefine both interfaces in C-Style, based on the values entered within Stateflow:

typedef enum
{
NS2_MYCLASS_MY_ENUM_FOO = 0,
NS2_MYCLASS_MY_ENUM_BAR = 1,
NS2_MYCLASS_MY_ENUM_FOOBAR = 2,
} MY_ENUM

typedef enum
{
NS2_MYOTHERCLASS_MY_OTHER_ENUM_FOO = 0,
NS2_MYOTHERCLASS_MY_OTHER_ENUM_BAR = 1,
NS2_MYOTHERCLASS_MY_OTHER_ENUM_FOOBAR = 2,
} MY_OTHER_ENUM

To get these values back on variables, defined in the form of the original enums, there have to be static casts implemented like e.g.

instanceOfMyClass.m_myEnum = static_cast<ns1::MyClass::myEnum>(MY_ENUM)
instanceOfMyOtherClass.m_myOtherEnum = static_cast<ns2::MyOtherClass::myOtherEnum>(MY_OTHER_ENUM)

The disadvantages are:

* redefinition is extra work, prone to errors and a lot of work to keep in sync
* static casting is dangerous, especially, when e.g. the order of the values within the enumeration changes
* the principle of single source of truth is violated which leads to these disadvantages

*Question:*

* Is there a way to directly use the original, C++-Style enums without recreating them and having all the stated disadvantages?
* I’m thinking about a solution where I can enter the names and paths of the original interfaces directly within StateFlow and generating C++ code that is therefore already integrated – without any wrapping and extra, error prone work. stateflow, c++, enum, namespace, class MATLAB Answers — New Questions

​

I was trying to run a PIL Simulation using Simulink. When deploying my controller subsystem to Arduino everything worked perfectly until it tried to build the PIL block for my controller and i get error [Cannot open output file "E:DocumentsUsersCodePID0_pbs.mexw64"] and the PIL block can’t be build. Can someone help me how to solve this error?I was trying to run a PIL Simulation using Simulink. When deploying my controller subsystem to Arduino everything worked perfectly until it tried to build the PIL block for my controller and i get error [Cannot open output file "E:DocumentsUsersCodePID0_pbs.mexw64"] and the PIL block can’t be build. Can someone help me how to solve this error? I was trying to run a PIL Simulation using Simulink. When deploying my controller subsystem to Arduino everything worked perfectly until it tried to build the PIL block for my controller and i get error [Cannot open output file "E:DocumentsUsersCodePID0_pbs.mexw64"] and the PIL block can’t be build. Can someone help me how to solve this error? pil, simulink, pid, pil block MATLAB Answers — New Questions

​

Hi I have the following code here, in which I was trying to train an LSTM to classify input data to catergorical lables as see below:
I get the following error
Error using trainnet (line 46)
Number of observations in predictors (1) and targets (897) must match. Check that the data and network are
consistent.

Error in LSTMGomz (line 40)
net = trainnet(XTrain,TTrain,layers,"crossentropy",options);

>>

Please help where did I go wrong

label = strings(997,1);
label(1:200) = ‘graphtype1’;
label(201:399) = ‘graphtype2’;
label(400:598) = ‘graphtype3’;
label(599:798) = ‘graphtype4’;
label(799:997) = ‘graphtype5’;
className = categorical(label);
className2 = categories(className);

Datain = xlsread(‘C:UsersernesOneDriveDocumentsMATLABLSTMdataIn.xlsx’);

% Above Datain has 997 graphs each with 100 samples
% E.g for graphs Datain(1:200,:) – graphtype 1
% graphs Datain(201:399) – graphtype 2 and so on
%So my objective is to train my LSTM using the graphs to labels
numObservations = 997;
[idxTrain,idxTest] = trainingPartitions(numObservations,[0.9 0.1]);
XTrain = Datain(idxTrain,:);% in Xtrain – there are 897 graphs each with 100 values, so
% Xtrain is 897 x 100,
TTrain = className(idxTrain,:);
numHiddenUnits = 120;
numClasses = 5;
layers = [
sequenceInputLayer(100) % I am not sure about this input, because my data comes in 1 by 100 arrys of a seq
%,with 1 – 100 ms timestamps
bilstmLayer(numHiddenUnits,OutputMode="last")
fullyConnectedLayer(numClasses)
softmaxLayer]
options = trainingOptions("adam", …
MaxEpochs=200, …
InitialLearnRate=0.002,…
GradientThreshold=1, …
Shuffle="never", …
Plots="training-progress", …
Metrics="accuracy", …
Verbose=false);
net = trainnet(XTrain,TTrain,layers,"crossentropy",options);Hi I have the following code here, in which I was trying to train an LSTM to classify input data to catergorical lables as see below:
I get the following error
Error using trainnet (line 46)
Number of observations in predictors (1) and targets (897) must match. Check that the data and network are
consistent.

Error in LSTMGomz (line 40)
net = trainnet(XTrain,TTrain,layers,"crossentropy",options);

>>

Please help where did I go wrong

label = strings(997,1);
label(1:200) = ‘graphtype1’;
label(201:399) = ‘graphtype2’;
label(400:598) = ‘graphtype3’;
label(599:798) = ‘graphtype4’;
label(799:997) = ‘graphtype5’;
className = categorical(label);
className2 = categories(className);

Datain = xlsread(‘C:UsersernesOneDriveDocumentsMATLABLSTMdataIn.xlsx’);

% Above Datain has 997 graphs each with 100 samples
% E.g for graphs Datain(1:200,:) – graphtype 1
% graphs Datain(201:399) – graphtype 2 and so on
%So my objective is to train my LSTM using the graphs to labels
numObservations = 997;
[idxTrain,idxTest] = trainingPartitions(numObservations,[0.9 0.1]);
XTrain = Datain(idxTrain,:);% in Xtrain – there are 897 graphs each with 100 values, so
% Xtrain is 897 x 100,
TTrain = className(idxTrain,:);
numHiddenUnits = 120;
numClasses = 5;
layers = [
sequenceInputLayer(100) % I am not sure about this input, because my data comes in 1 by 100 arrys of a seq
%,with 1 – 100 ms timestamps
bilstmLayer(numHiddenUnits,OutputMode="last")
fullyConnectedLayer(numClasses)
softmaxLayer]
options = trainingOptions("adam", …
MaxEpochs=200, …
InitialLearnRate=0.002,…
GradientThreshold=1, …
Shuffle="never", …
Plots="training-progress", …
Metrics="accuracy", …
Verbose=false);
net = trainnet(XTrain,TTrain,layers,"crossentropy",options); Hi I have the following code here, in which I was trying to train an LSTM to classify input data to catergorical lables as see below:
I get the following error
Error using trainnet (line 46)
Number of observations in predictors (1) and targets (897) must match. Check that the data and network are
consistent.

Error in LSTMGomz (line 40)
net = trainnet(XTrain,TTrain,layers,"crossentropy",options);

>>

Please help where did I go wrong

label = strings(997,1);
label(1:200) = ‘graphtype1’;
label(201:399) = ‘graphtype2’;
label(400:598) = ‘graphtype3’;
label(599:798) = ‘graphtype4’;
label(799:997) = ‘graphtype5’;
className = categorical(label);
className2 = categories(className);

Datain = xlsread(‘C:UsersernesOneDriveDocumentsMATLABLSTMdataIn.xlsx’);

% Above Datain has 997 graphs each with 100 samples
% E.g for graphs Datain(1:200,:) – graphtype 1
% graphs Datain(201:399) – graphtype 2 and so on
%So my objective is to train my LSTM using the graphs to labels
numObservations = 997;
[idxTrain,idxTest] = trainingPartitions(numObservations,[0.9 0.1]);
XTrain = Datain(idxTrain,:);% in Xtrain – there are 897 graphs each with 100 values, so
% Xtrain is 897 x 100,
TTrain = className(idxTrain,:);
numHiddenUnits = 120;
numClasses = 5;
layers = [
sequenceInputLayer(100) % I am not sure about this input, because my data comes in 1 by 100 arrys of a seq
%,with 1 – 100 ms timestamps
bilstmLayer(numHiddenUnits,OutputMode="last")
fullyConnectedLayer(numClasses)
softmaxLayer]
options = trainingOptions("adam", …
MaxEpochs=200, …
InitialLearnRate=0.002,…
GradientThreshold=1, …
Shuffle="never", …
Plots="training-progress", …
Metrics="accuracy", …
Verbose=false);
net = trainnet(XTrain,TTrain,layers,"crossentropy",options); lstm sequence to text MATLAB Answers — New Questions

​

I use the EMACS key bindings in the Matlab editor (even on Windows). But I can’t find a way to change the key bindings for the App Designer. My fingers are used to the EMACS bindings and this is driving me crazy!
Is there a some way of changing the App Designer key bindings that I am missing? Thanks!I use the EMACS key bindings in the Matlab editor (even on Windows). But I can’t find a way to change the key bindings for the App Designer. My fingers are used to the EMACS bindings and this is driving me crazy!
Is there a some way of changing the App Designer key bindings that I am missing? Thanks! I use the EMACS key bindings in the Matlab editor (even on Windows). But I can’t find a way to change the key bindings for the App Designer. My fingers are used to the EMACS bindings and this is driving me crazy!
Is there a some way of changing the App Designer key bindings that I am missing? Thanks! appdesigner MATLAB Answers — New Questions

​

I want to train a vector multi-input, multi-output network, but I get the error “Number of input data formats (1) and number of network inputs (2) must match”. This is the code,
clear
input1ds=signalDatastore("input1.csv")
input2ds=signalDatastore("input2.csv")
output1ds=signalDatastore("output1.csv")
output2ds=signalDatastore("output2.csv")
ds=combine(input1ds,input2ds,output1ds,output2ds)
isShuffleable(ds)

% 入力とターゲット
% x = [[0:0.1:10]’ [0:0.1:10]’]
% t = sin(x)

net=dlnetwork

% ニューラルネットワークの定義
layers1 = [
featureInputLayer(1,"name","input1")
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(1) % 1ユニットの全結合層
];
layers2 = [
featureInputLayer(1,"Name","input2")
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(1) % 1ユニットの全結合層
];

net=addLayers(net,layers1)
net=addLayers(net,layers2)
net.plot
% layers=layerGraph()
% layers=addLayers(layers,layers1)
% layers=addLayers(layers,layers2)

% オプションの設定
options = trainingOptions(‘sgdm’, … % 最適化アルゴリズム
‘MaxEpochs’, 500, … % 最大エポック数
‘MiniBatchSize’, 2^3, … % ミニバッチサイズ
‘Verbose’, true,…% 進行状況の表示
‘InputDataFormats’, {‘SB’} …
)

% ニューラルネットワークのトレーニング
customLossFunction = @(Y, T) customloss(Y, T);
net = trainnet(ds,net,customLossFunction,options) % x,tは縦ベクトル

The “input1.csv” and “output1.csv” all contain a single column of vertical vectors. This time, it is a 2-input, 2-output network.
The function “customloss” is a function I defined myself. This is the error statement.

Error using trainnet (line 46)
Number of input data formats (1) and number of network inputs (2) must match.

Error in parallel_learn_test (line 53)
net = trainnet(ds,net,customLossFunction,options) % x,tは縦ベクトル

What is wrong? And what solutions are available?I want to train a vector multi-input, multi-output network, but I get the error “Number of input data formats (1) and number of network inputs (2) must match”. This is the code,
clear
input1ds=signalDatastore("input1.csv")
input2ds=signalDatastore("input2.csv")
output1ds=signalDatastore("output1.csv")
output2ds=signalDatastore("output2.csv")
ds=combine(input1ds,input2ds,output1ds,output2ds)
isShuffleable(ds)

% 入力とターゲット
% x = [[0:0.1:10]’ [0:0.1:10]’]
% t = sin(x)

net=dlnetwork

% ニューラルネットワークの定義
layers1 = [
featureInputLayer(1,"name","input1")
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(1) % 1ユニットの全結合層
];
layers2 = [
featureInputLayer(1,"Name","input2")
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(1) % 1ユニットの全結合層
];

net=addLayers(net,layers1)
net=addLayers(net,layers2)
net.plot
% layers=layerGraph()
% layers=addLayers(layers,layers1)
% layers=addLayers(layers,layers2)

% オプションの設定
options = trainingOptions(‘sgdm’, … % 最適化アルゴリズム
‘MaxEpochs’, 500, … % 最大エポック数
‘MiniBatchSize’, 2^3, … % ミニバッチサイズ
‘Verbose’, true,…% 進行状況の表示
‘InputDataFormats’, {‘SB’} …
)

% ニューラルネットワークのトレーニング
customLossFunction = @(Y, T) customloss(Y, T);
net = trainnet(ds,net,customLossFunction,options) % x,tは縦ベクトル

The “input1.csv” and “output1.csv” all contain a single column of vertical vectors. This time, it is a 2-input, 2-output network.
The function “customloss” is a function I defined myself. This is the error statement.

Error using trainnet (line 46)
Number of input data formats (1) and number of network inputs (2) must match.

Error in parallel_learn_test (line 53)
net = trainnet(ds,net,customLossFunction,options) % x,tは縦ベクトル

What is wrong? And what solutions are available? I want to train a vector multi-input, multi-output network, but I get the error “Number of input data formats (1) and number of network inputs (2) must match”. This is the code,
clear
input1ds=signalDatastore("input1.csv")
input2ds=signalDatastore("input2.csv")
output1ds=signalDatastore("output1.csv")
output2ds=signalDatastore("output2.csv")
ds=combine(input1ds,input2ds,output1ds,output2ds)
isShuffleable(ds)

% 入力とターゲット
% x = [[0:0.1:10]’ [0:0.1:10]’]
% t = sin(x)

net=dlnetwork

% ニューラルネットワークの定義
layers1 = [
featureInputLayer(1,"name","input1")
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(1) % 1ユニットの全結合層
];
layers2 = [
featureInputLayer(1,"Name","input2")
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(1) % 1ユニットの全結合層
];

net=addLayers(net,layers1)
net=addLayers(net,layers2)
net.plot
% layers=layerGraph()
% layers=addLayers(layers,layers1)
% layers=addLayers(layers,layers2)

% オプションの設定
options = trainingOptions(‘sgdm’, … % 最適化アルゴリズム
‘MaxEpochs’, 500, … % 最大エポック数
‘MiniBatchSize’, 2^3, … % ミニバッチサイズ
‘Verbose’, true,…% 進行状況の表示
‘InputDataFormats’, {‘SB’} …
)

% ニューラルネットワークのトレーニング
customLossFunction = @(Y, T) customloss(Y, T);
net = trainnet(ds,net,customLossFunction,options) % x,tは縦ベクトル

The “input1.csv” and “output1.csv” all contain a single column of vertical vectors. This time, it is a 2-input, 2-output network.
The function “customloss” is a function I defined myself. This is the error statement.

Error using trainnet (line 46)
Number of input data formats (1) and number of network inputs (2) must match.

Error in parallel_learn_test (line 53)
net = trainnet(ds,net,customLossFunction,options) % x,tは縦ベクトル

What is wrong? And what solutions are available? deep learning, neural networks MATLAB Answers — New Questions

​

I am studying how to do multidimensional scaling in matlab. I am able to reproduce the output (2D plot) from a published example (http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/122-multidimensional-scaling-essentials-algorithms-and-r-code/).
The output is based on the data table found in the Swiss Fertility and Socioeconomic Indicators (1888) (https://rpubs.com/krishnakuyate/Swiss_FS_Data).
I constructed a 47×47 array of Euclidean distances (D) of the Swiss Fertility and Socioeconomic Indicators and ran Y=cmdscale(D,2) to obtain a plot of these data:

My plot looks very similar (but inverted mirror image) to the published plot from http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/122-multidimensional-scaling-essentials-algorithms-and-r-code/:

But how can I get matlab to label the points in my plot as is done above in the published figure?I am studying how to do multidimensional scaling in matlab. I am able to reproduce the output (2D plot) from a published example (http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/122-multidimensional-scaling-essentials-algorithms-and-r-code/).
The output is based on the data table found in the Swiss Fertility and Socioeconomic Indicators (1888) (https://rpubs.com/krishnakuyate/Swiss_FS_Data).
I constructed a 47×47 array of Euclidean distances (D) of the Swiss Fertility and Socioeconomic Indicators and ran Y=cmdscale(D,2) to obtain a plot of these data:

My plot looks very similar (but inverted mirror image) to the published plot from http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/122-multidimensional-scaling-essentials-algorithms-and-r-code/:

But how can I get matlab to label the points in my plot as is done above in the published figure? I am studying how to do multidimensional scaling in matlab. I am able to reproduce the output (2D plot) from a published example (http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/122-multidimensional-scaling-essentials-algorithms-and-r-code/).
The output is based on the data table found in the Swiss Fertility and Socioeconomic Indicators (1888) (https://rpubs.com/krishnakuyate/Swiss_FS_Data).
I constructed a 47×47 array of Euclidean distances (D) of the Swiss Fertility and Socioeconomic Indicators and ran Y=cmdscale(D,2) to obtain a plot of these data:

My plot looks very similar (but inverted mirror image) to the published plot from http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/122-multidimensional-scaling-essentials-algorithms-and-r-code/:

But how can I get matlab to label the points in my plot as is done above in the published figure? multidimensional scaliing, 2d plot, label plot MATLAB Answers — New Questions

​

when i try to install Simulink in Matlab an error is coming, something unexpected occur, try rerunning Matlabwhen i try to install Simulink in Matlab an error is coming, something unexpected occur, try rerunning Matlab when i try to install Simulink in Matlab an error is coming, something unexpected occur, try rerunning Matlab simulink MATLAB Answers — New Questions

​