Hello,
Im working to minimise the total cost incurred to retailer by finding optimum value of maximum inventory level using Genetic algorithm. Im considering 4 subcosts (procurment, holding, expiry, lost sales) which have fixed rate (per unit qty). I have to do this over a time period (x days). Ive already done it over different time duration (1 month, and 3 yr). Each day in this time period has pre-determined demand (ie. units sold), taken from CSVDemand file.
How to conduct "senstivity analysis"? I tried searching and understood that i should record changes in result based on the changes in GA parameters. However, on varying the mutation function, selection function, mutation rate, population size, etc im getting the same results. No variation. Is that because of only 1 optimisation variable?
How should i do senstivity analysis then?
Ive attached the code below. TCFunc is the Objective function. GA_1 is the GA code. it was made with help of optimtask insert.
please provide some guidance.Hello,
Im working to minimise the total cost incurred to retailer by finding optimum value of maximum inventory level using Genetic algorithm. Im considering 4 subcosts (procurment, holding, expiry, lost sales) which have fixed rate (per unit qty). I have to do this over a time period (x days). Ive already done it over different time duration (1 month, and 3 yr). Each day in this time period has pre-determined demand (ie. units sold), taken from CSVDemand file.
How to conduct "senstivity analysis"? I tried searching and understood that i should record changes in result based on the changes in GA parameters. However, on varying the mutation function, selection function, mutation rate, population size, etc im getting the same results. No variation. Is that because of only 1 optimisation variable?
How should i do senstivity analysis then?
Ive attached the code below. TCFunc is the Objective function. GA_1 is the GA code. it was made with help of optimtask insert.
please provide some guidance. Hello,
Im working to minimise the total cost incurred to retailer by finding optimum value of maximum inventory level using Genetic algorithm. Im considering 4 subcosts (procurment, holding, expiry, lost sales) which have fixed rate (per unit qty). I have to do this over a time period (x days). Ive already done it over different time duration (1 month, and 3 yr). Each day in this time period has pre-determined demand (ie. units sold), taken from CSVDemand file.
How to conduct "senstivity analysis"? I tried searching and understood that i should record changes in result based on the changes in GA parameters. However, on varying the mutation function, selection function, mutation rate, population size, etc im getting the same results. No variation. Is that because of only 1 optimisation variable?
How should i do senstivity analysis then?
Ive attached the code below. TCFunc is the Objective function. GA_1 is the GA code. it was made with help of optimtask insert.
please provide some guidance. senstivity analysis, genetic algorithm, optimization MATLAB Answers — New Questions

​

Suppose I have a table, T:
A B C
1 6 10
2 5 20
3 4 30
4 3 45
5 2 6
6 1 10

I want to subtract each entry in column C where the A and B values are flipped, and put this value into column D
For example, the first row (1, 6, 10) – the last row (6, 1, 10); (2, 5, 20) – (5, 2, 6) and so on
So D should be:
0
14
-15
15
-14
0

Thanks a lot!

Edit: (i) Flipped rows could occur anywhere in the table; (ii) there may be repeated values in Α and BSuppose I have a table, T:
A B C
1 6 10
2 5 20
3 4 30
4 3 45
5 2 6
6 1 10

I want to subtract each entry in column C where the A and B values are flipped, and put this value into column D
For example, the first row (1, 6, 10) – the last row (6, 1, 10); (2, 5, 20) – (5, 2, 6) and so on
So D should be:
0
14
-15
15
-14
0

Thanks a lot!

Edit: (i) Flipped rows could occur anywhere in the table; (ii) there may be repeated values in Α and B Suppose I have a table, T:
A B C
1 6 10
2 5 20
3 4 30
4 3 45
5 2 6
6 1 10

I want to subtract each entry in column C where the A and B values are flipped, and put this value into column D
For example, the first row (1, 6, 10) – the last row (6, 1, 10); (2, 5, 20) – (5, 2, 6) and so on
So D should be:
0
14
-15
15
-14
0

Thanks a lot!

Edit: (i) Flipped rows could occur anywhere in the table; (ii) there may be repeated values in Α and B subtraction, tables values MATLAB Answers — New Questions

​

FDMex()
function FDMex()
N = 100;
lgth = 1.0;
h = lgth/N;
eta = 0:h:lgth;
Ac = 0.0001;
S = 0.2;
k = 0.1;
Pr = 1.0;
Sc = 1.2;
alpha1 = 0.4;
alpha2 = 0;
zeta = 0.3;
gamma = 0.3;
omega = 0.4;
fw = 0.2;
F = zeros(N+2, 1);
G = zeros(N+2, 1);
theta = zeros(N+2, 1);
phi = zeros(N+2, 1);
H = zeros(N+2, 1);
F(1) = 0;
G(1) = omega;
theta(1) = 1;
phi(1) = 1;
H(1) = S + fw / Sc * (phi(2) – phi(1)) / h^2;
F(N+2) = 0;
G(N+2) = 0;
theta(N+2) = 0;
phi(N+2) = 0;
c = 1.0;
while(c>0)
[H1,F1,G1,theta1,phi1] = equation(H,F,G,theta,phi,N,h);
c = 0.0;
for i = 2:N-1
if (abs((H(i)-H1(i)), (F(i) – F1(i)), (G(i) – G1(i)),(theta(i) – theta1(i)), (phi(i) – phi1(i)))>Ac)
c = c+1;
break
end
end
H = H1;
F = F1;
G = G1;
theta = theta1;
phi = phi1;
end
disp(‘Hence solutions = :’ );
H2(1 : N+2) = H;
F2(1 : N+2) = F;
G2(1 : N+2) = G;
theta2(1 : N+2) = theta;
phi2(1 : N+2) = phi;
eta = 0:h:lgth;
figure(1)
plot(eta,H2,’*r’)
hold on
function [H1,F1,G1,theta1,phi1] = equation(H,F,G,theta,phi,N,h)
for i = 2:N-1
H(i+1) = H(i) – h*2*F(i);
F(i+1) = (F(i) + F(i+2))/2 -H(i)*(h/2)*(F(i+1)-F(i)) +(h^2)*(G(i)^2) – (h^2)*(F(i)^2) + (h^2)*(S/2)*((((i*h)+1)/2)*((F(i+1)-F(i))/h)+F(i)) – (h^2)*(k/2)*(((G(i+1)-G(i))/h)^2 – ((F(i+1)-F(i))/h)^2+2*F((F(i) -2*F(i+1) + F(i+2))/h^2));
G(i+1) = 2*G(i) – G(i+2) -(h/2)*H(i)*(G(i+1)-G(i+2)) + 2*F(i)*G(i) – (h^2)*S*(((i*h+1)/2)*((G(i+1)-G(i+2))/2*h)+G(i)) + (h^2)*2*k*(F(i)*((G(i+1) -2*G(i) + G(i+2))/h^2) – ((F(i+1)-F(i+2))/2*h)*((G(i+1)-G(i+2))/2*h));
theta(i+1) = 2*theta(i) – theta(i+2) + Pr*(h/2)*(theta(i+1)-theta(i+2)) – Pr*(h^2)*S*(((i*h+1)/2)*((theta(i+1)-theta(i+2))/2*h)+alpha1*theta(i)) + zeta*((theta(i+1) -2*theta(i) + theta(i+2))/h^2 – 2*F(i)*G(i)*((theta(i+1)-theta(i+2))/2*h)); phi(i+1) = 2*phi(i) – phi(i+2) + Sc*(h/2)*(theta(i+1)-theta(i+2)) – Sc*(h^2)*S*(((i*h+1)/2)*((phi(i+1)-theta(i+2))/2*h)+alpha2*phi(i)) + Sc*(h^2)*gamma*phi(i);
end
H1(1) = H(1);
F1(1) = F(1);
G1(1) = G(1);
theta1(1) = theta(1);
phi1(1) = phi(1);
F1(N+2) = F(N+2);
G1(N+2) = G(N+2);
theta1(N+2) = theta(N+2);
phi1(N+2) = phi(N+2);
end
end
AArray indices must be positive integers or logical values.FDMex()
function FDMex()
N = 100;
lgth = 1.0;
h = lgth/N;
eta = 0:h:lgth;
Ac = 0.0001;
S = 0.2;
k = 0.1;
Pr = 1.0;
Sc = 1.2;
alpha1 = 0.4;
alpha2 = 0;
zeta = 0.3;
gamma = 0.3;
omega = 0.4;
fw = 0.2;
F = zeros(N+2, 1);
G = zeros(N+2, 1);
theta = zeros(N+2, 1);
phi = zeros(N+2, 1);
H = zeros(N+2, 1);
F(1) = 0;
G(1) = omega;
theta(1) = 1;
phi(1) = 1;
H(1) = S + fw / Sc * (phi(2) – phi(1)) / h^2;
F(N+2) = 0;
G(N+2) = 0;
theta(N+2) = 0;
phi(N+2) = 0;
c = 1.0;
while(c>0)
[H1,F1,G1,theta1,phi1] = equation(H,F,G,theta,phi,N,h);
c = 0.0;
for i = 2:N-1
if (abs((H(i)-H1(i)), (F(i) – F1(i)), (G(i) – G1(i)),(theta(i) – theta1(i)), (phi(i) – phi1(i)))>Ac)
c = c+1;
break
end
end
H = H1;
F = F1;
G = G1;
theta = theta1;
phi = phi1;
end
disp(‘Hence solutions = :’ );
H2(1 : N+2) = H;
F2(1 : N+2) = F;
G2(1 : N+2) = G;
theta2(1 : N+2) = theta;
phi2(1 : N+2) = phi;
eta = 0:h:lgth;
figure(1)
plot(eta,H2,’*r’)
hold on
function [H1,F1,G1,theta1,phi1] = equation(H,F,G,theta,phi,N,h)
for i = 2:N-1
H(i+1) = H(i) – h*2*F(i);
F(i+1) = (F(i) + F(i+2))/2 -H(i)*(h/2)*(F(i+1)-F(i)) +(h^2)*(G(i)^2) – (h^2)*(F(i)^2) + (h^2)*(S/2)*((((i*h)+1)/2)*((F(i+1)-F(i))/h)+F(i)) – (h^2)*(k/2)*(((G(i+1)-G(i))/h)^2 – ((F(i+1)-F(i))/h)^2+2*F((F(i) -2*F(i+1) + F(i+2))/h^2));
G(i+1) = 2*G(i) – G(i+2) -(h/2)*H(i)*(G(i+1)-G(i+2)) + 2*F(i)*G(i) – (h^2)*S*(((i*h+1)/2)*((G(i+1)-G(i+2))/2*h)+G(i)) + (h^2)*2*k*(F(i)*((G(i+1) -2*G(i) + G(i+2))/h^2) – ((F(i+1)-F(i+2))/2*h)*((G(i+1)-G(i+2))/2*h));
theta(i+1) = 2*theta(i) – theta(i+2) + Pr*(h/2)*(theta(i+1)-theta(i+2)) – Pr*(h^2)*S*(((i*h+1)/2)*((theta(i+1)-theta(i+2))/2*h)+alpha1*theta(i)) + zeta*((theta(i+1) -2*theta(i) + theta(i+2))/h^2 – 2*F(i)*G(i)*((theta(i+1)-theta(i+2))/2*h)); phi(i+1) = 2*phi(i) – phi(i+2) + Sc*(h/2)*(theta(i+1)-theta(i+2)) – Sc*(h^2)*S*(((i*h+1)/2)*((phi(i+1)-theta(i+2))/2*h)+alpha2*phi(i)) + Sc*(h^2)*gamma*phi(i);
end
H1(1) = H(1);
F1(1) = F(1);
G1(1) = G(1);
theta1(1) = theta(1);
phi1(1) = phi(1);
F1(N+2) = F(N+2);
G1(N+2) = G(N+2);
theta1(N+2) = theta(N+2);
phi1(N+2) = phi(N+2);
end
end
AArray indices must be positive integers or logical values. FDMex()
function FDMex()
N = 100;
lgth = 1.0;
h = lgth/N;
eta = 0:h:lgth;
Ac = 0.0001;
S = 0.2;
k = 0.1;
Pr = 1.0;
Sc = 1.2;
alpha1 = 0.4;
alpha2 = 0;
zeta = 0.3;
gamma = 0.3;
omega = 0.4;
fw = 0.2;
F = zeros(N+2, 1);
G = zeros(N+2, 1);
theta = zeros(N+2, 1);
phi = zeros(N+2, 1);
H = zeros(N+2, 1);
F(1) = 0;
G(1) = omega;
theta(1) = 1;
phi(1) = 1;
H(1) = S + fw / Sc * (phi(2) – phi(1)) / h^2;
F(N+2) = 0;
G(N+2) = 0;
theta(N+2) = 0;
phi(N+2) = 0;
c = 1.0;
while(c>0)
[H1,F1,G1,theta1,phi1] = equation(H,F,G,theta,phi,N,h);
c = 0.0;
for i = 2:N-1
if (abs((H(i)-H1(i)), (F(i) – F1(i)), (G(i) – G1(i)),(theta(i) – theta1(i)), (phi(i) – phi1(i)))>Ac)
c = c+1;
break
end
end
H = H1;
F = F1;
G = G1;
theta = theta1;
phi = phi1;
end
disp(‘Hence solutions = :’ );
H2(1 : N+2) = H;
F2(1 : N+2) = F;
G2(1 : N+2) = G;
theta2(1 : N+2) = theta;
phi2(1 : N+2) = phi;
eta = 0:h:lgth;
figure(1)
plot(eta,H2,’*r’)
hold on
function [H1,F1,G1,theta1,phi1] = equation(H,F,G,theta,phi,N,h)
for i = 2:N-1
H(i+1) = H(i) – h*2*F(i);
F(i+1) = (F(i) + F(i+2))/2 -H(i)*(h/2)*(F(i+1)-F(i)) +(h^2)*(G(i)^2) – (h^2)*(F(i)^2) + (h^2)*(S/2)*((((i*h)+1)/2)*((F(i+1)-F(i))/h)+F(i)) – (h^2)*(k/2)*(((G(i+1)-G(i))/h)^2 – ((F(i+1)-F(i))/h)^2+2*F((F(i) -2*F(i+1) + F(i+2))/h^2));
G(i+1) = 2*G(i) – G(i+2) -(h/2)*H(i)*(G(i+1)-G(i+2)) + 2*F(i)*G(i) – (h^2)*S*(((i*h+1)/2)*((G(i+1)-G(i+2))/2*h)+G(i)) + (h^2)*2*k*(F(i)*((G(i+1) -2*G(i) + G(i+2))/h^2) – ((F(i+1)-F(i+2))/2*h)*((G(i+1)-G(i+2))/2*h));
theta(i+1) = 2*theta(i) – theta(i+2) + Pr*(h/2)*(theta(i+1)-theta(i+2)) – Pr*(h^2)*S*(((i*h+1)/2)*((theta(i+1)-theta(i+2))/2*h)+alpha1*theta(i)) + zeta*((theta(i+1) -2*theta(i) + theta(i+2))/h^2 – 2*F(i)*G(i)*((theta(i+1)-theta(i+2))/2*h)); phi(i+1) = 2*phi(i) – phi(i+2) + Sc*(h/2)*(theta(i+1)-theta(i+2)) – Sc*(h^2)*S*(((i*h+1)/2)*((phi(i+1)-theta(i+2))/2*h)+alpha2*phi(i)) + Sc*(h^2)*gamma*phi(i);
end
H1(1) = H(1);
F1(1) = F(1);
G1(1) = G(1);
theta1(1) = theta(1);
phi1(1) = phi(1);
F1(N+2) = F(N+2);
G1(N+2) = G(N+2);
theta1(N+2) = theta(N+2);
phi1(N+2) = phi(N+2);
end
end
AArray indices must be positive integers or logical values. fdm MATLAB Answers — New Questions

​

Hello everyone,

I’m still on the modeling of gravitational lensing.
I intended to proceed in a different method. Let me explain.

I intend to take a picture, for example, a sky with a galaxy. And model the image by a matrix. This matrix would actually be of size N * M (where N is the pixel length of the image and M, the width)
Each pixel would therefore a number of the matrix.

I have two perspectives:

First, create a map as modeling the geometric changes applied to our image (to represent the bending of light rays) and then apply to the image.

The second, operate directly on the image without using a tier modeling.

My result should look like this:

http://www.epm6604b.be/lentille/images/imginfluence/m31_trou_noir.jpg

A=imread(‘galaxy.jpg’)
% Liste des Variables
[nr,nc]= size([A])
x1=y1=500 % Position de la masse défléctrice
R= 1 %Rayon de la masse déflectrice (exemple de trou noir = 1 pixel)
largeur = nc %largeur de l’image
longeur = nr %longueur de l’image
Ralpha=(sqrt((x-x1).^2+(y-y1).^2))
L=(4.*G*M)./(c^2)

% Les Constantes

G= 6.67e-11
c=3.0e+8
M= 1,7e+38 %Masse de la masse déflectrice
z2 = 9,5e+15 %Distance de la masse par rapport à la galaxie
z1= 2,75e+22 %Distance par rapport à la Terre

% Lentille gravitationnelle
B=[]
for i=1:largeur
for j= 1:longueur
if sqrt((x-x1).^2+(y-y1).^2)<=R:
A(i,j)= 0 % pixel noir
if sqrt((x-x1).^2+(y-y1).^2)>R:
x[i]=i+z2.*((i./z1)-(L/R).*i/R)
y[j]=j+z2.*((j./z1)-(L/R).*j/R)
A(i,j)=B(x[i],y[j])
imread(B)
end
% code

I tried to do it for transformations. If i try to change A(i,j) to A(x4,y4) i will change the original pixel wich locate in (x,y) but when the programm will go to A(x4,y4) throught i,j coordonate.
The pixel itself has already been moved, it will not change the original pixel but the pixel from (i, j) already transformed. So how can we apply transformations while telling it not to apply transformations on the transformed pixel but the original pixel in this position previously.
Here i try to create an other matrix B which contains all transformation (or value pixel with relocation).

Next problem : Quality picture:

If i apply these transformations in A matrix, i will lose in image quality? Because when i transformed my image in A matrix, Octave noticed that :

"Warning : Your Version of GraphicsMagick limits images to 8 bits per pixel"

That’s why i would like to know if it could be better to call the image in an other way to keep the original quality picture. But if i must do that, i don’t know how… :/

Excuse me for my expression, but i’m not english.

Thx in AdvanceHello everyone,

I’m still on the modeling of gravitational lensing.
I intended to proceed in a different method. Let me explain.

I intend to take a picture, for example, a sky with a galaxy. And model the image by a matrix. This matrix would actually be of size N * M (where N is the pixel length of the image and M, the width)
Each pixel would therefore a number of the matrix.

I have two perspectives:

First, create a map as modeling the geometric changes applied to our image (to represent the bending of light rays) and then apply to the image.

The second, operate directly on the image without using a tier modeling.

My result should look like this:

http://www.epm6604b.be/lentille/images/imginfluence/m31_trou_noir.jpg

A=imread(‘galaxy.jpg’)
% Liste des Variables
[nr,nc]= size([A])
x1=y1=500 % Position de la masse défléctrice
R= 1 %Rayon de la masse déflectrice (exemple de trou noir = 1 pixel)
largeur = nc %largeur de l’image
longeur = nr %longueur de l’image
Ralpha=(sqrt((x-x1).^2+(y-y1).^2))
L=(4.*G*M)./(c^2)

% Les Constantes

G= 6.67e-11
c=3.0e+8
M= 1,7e+38 %Masse de la masse déflectrice
z2 = 9,5e+15 %Distance de la masse par rapport à la galaxie
z1= 2,75e+22 %Distance par rapport à la Terre

% Lentille gravitationnelle
B=[]
for i=1:largeur
for j= 1:longueur
if sqrt((x-x1).^2+(y-y1).^2)<=R:
A(i,j)= 0 % pixel noir
if sqrt((x-x1).^2+(y-y1).^2)>R:
x[i]=i+z2.*((i./z1)-(L/R).*i/R)
y[j]=j+z2.*((j./z1)-(L/R).*j/R)
A(i,j)=B(x[i],y[j])
imread(B)
end
% code

I tried to do it for transformations. If i try to change A(i,j) to A(x4,y4) i will change the original pixel wich locate in (x,y) but when the programm will go to A(x4,y4) throught i,j coordonate.
The pixel itself has already been moved, it will not change the original pixel but the pixel from (i, j) already transformed. So how can we apply transformations while telling it not to apply transformations on the transformed pixel but the original pixel in this position previously.
Here i try to create an other matrix B which contains all transformation (or value pixel with relocation).

Next problem : Quality picture:

If i apply these transformations in A matrix, i will lose in image quality? Because when i transformed my image in A matrix, Octave noticed that :

"Warning : Your Version of GraphicsMagick limits images to 8 bits per pixel"

That’s why i would like to know if it could be better to call the image in an other way to keep the original quality picture. But if i must do that, i don’t know how… :/

Excuse me for my expression, but i’m not english.

Thx in Advance Hello everyone,

I’m still on the modeling of gravitational lensing.
I intended to proceed in a different method. Let me explain.

I intend to take a picture, for example, a sky with a galaxy. And model the image by a matrix. This matrix would actually be of size N * M (where N is the pixel length of the image and M, the width)
Each pixel would therefore a number of the matrix.

I have two perspectives:

First, create a map as modeling the geometric changes applied to our image (to represent the bending of light rays) and then apply to the image.

The second, operate directly on the image without using a tier modeling.

My result should look like this:

http://www.epm6604b.be/lentille/images/imginfluence/m31_trou_noir.jpg

A=imread(‘galaxy.jpg’)
% Liste des Variables
[nr,nc]= size([A])
x1=y1=500 % Position de la masse défléctrice
R= 1 %Rayon de la masse déflectrice (exemple de trou noir = 1 pixel)
largeur = nc %largeur de l’image
longeur = nr %longueur de l’image
Ralpha=(sqrt((x-x1).^2+(y-y1).^2))
L=(4.*G*M)./(c^2)

% Les Constantes

G= 6.67e-11
c=3.0e+8
M= 1,7e+38 %Masse de la masse déflectrice
z2 = 9,5e+15 %Distance de la masse par rapport à la galaxie
z1= 2,75e+22 %Distance par rapport à la Terre

% Lentille gravitationnelle
B=[]
for i=1:largeur
for j= 1:longueur
if sqrt((x-x1).^2+(y-y1).^2)<=R:
A(i,j)= 0 % pixel noir
if sqrt((x-x1).^2+(y-y1).^2)>R:
x[i]=i+z2.*((i./z1)-(L/R).*i/R)
y[j]=j+z2.*((j./z1)-(L/R).*j/R)
A(i,j)=B(x[i],y[j])
imread(B)
end
% code

I tried to do it for transformations. If i try to change A(i,j) to A(x4,y4) i will change the original pixel wich locate in (x,y) but when the programm will go to A(x4,y4) throught i,j coordonate.
The pixel itself has already been moved, it will not change the original pixel but the pixel from (i, j) already transformed. So how can we apply transformations while telling it not to apply transformations on the transformed pixel but the original pixel in this position previously.
Here i try to create an other matrix B which contains all transformation (or value pixel with relocation).

Next problem : Quality picture:

If i apply these transformations in A matrix, i will lose in image quality? Because when i transformed my image in A matrix, Octave noticed that :

"Warning : Your Version of GraphicsMagick limits images to 8 bits per pixel"

That’s why i would like to know if it could be better to call the image in an other way to keep the original quality picture. But if i must do that, i don’t know how… :/

Excuse me for my expression, but i’m not english.

Thx in Advance image processing, pixel, matrix, simulation MATLAB Answers — New Questions

​

I have attached the data and facing a following problem.
In a first step I am convolving a wav (101 1) with trc (101 3) e.g., a single wav is being convolved three trc (of equal number of rows) and giving three outputs. I am using following code and it is working:
synth = conv2(wav_near’, trc);
t0_i = floor(t0/dt); Nl = 101;
synth = synth(1+t0_i:Nl+t0_i,:);

Now, I have three wav (108 3) and want to convolve with trc (120 3) in way like the first wav will convolve with first trc, second wav with second trc and so on. I have attaced all the three wav (wav_near, wav_mid, wav_far) and trc. I tried a lot of combinations but not working. How can I change the above code in this case.
The size of outcomes should be the size of trc.
One way I tried like but results are not same

synth = zeros(120, 3);
% Loop through each column
for col = 1:3

conv_result = conv2(wav(:, col), trc(:, col), ‘full’)

start_idx = floor((length(wav(:, col)) – 1) / 2) + 1;
end_idx = start_idx + 120 – 1;

synth(:, col) = conv_result(start_idx:end_idx);
endI have attached the data and facing a following problem.
In a first step I am convolving a wav (101 1) with trc (101 3) e.g., a single wav is being convolved three trc (of equal number of rows) and giving three outputs. I am using following code and it is working:
synth = conv2(wav_near’, trc);
t0_i = floor(t0/dt); Nl = 101;
synth = synth(1+t0_i:Nl+t0_i,:);

Now, I have three wav (108 3) and want to convolve with trc (120 3) in way like the first wav will convolve with first trc, second wav with second trc and so on. I have attaced all the three wav (wav_near, wav_mid, wav_far) and trc. I tried a lot of combinations but not working. How can I change the above code in this case.
The size of outcomes should be the size of trc.
One way I tried like but results are not same

synth = zeros(120, 3);
% Loop through each column
for col = 1:3

conv_result = conv2(wav(:, col), trc(:, col), ‘full’)

start_idx = floor((length(wav(:, col)) – 1) / 2) + 1;
end_idx = start_idx + 120 – 1;

synth(:, col) = conv_result(start_idx:end_idx);
end I have attached the data and facing a following problem.
In a first step I am convolving a wav (101 1) with trc (101 3) e.g., a single wav is being convolved three trc (of equal number of rows) and giving three outputs. I am using following code and it is working:
synth = conv2(wav_near’, trc);
t0_i = floor(t0/dt); Nl = 101;
synth = synth(1+t0_i:Nl+t0_i,:);

Now, I have three wav (108 3) and want to convolve with trc (120 3) in way like the first wav will convolve with first trc, second wav with second trc and so on. I have attaced all the three wav (wav_near, wav_mid, wav_far) and trc. I tried a lot of combinations but not working. How can I change the above code in this case.
The size of outcomes should be the size of trc.
One way I tried like but results are not same

synth = zeros(120, 3);
% Loop through each column
for col = 1:3

conv_result = conv2(wav(:, col), trc(:, col), ‘full’)

start_idx = floor((length(wav(:, col)) – 1) / 2) + 1;
end_idx = start_idx + 120 – 1;

synth(:, col) = conv_result(start_idx:end_idx);
end matlab code, matlab, matrices MATLAB Answers — New Questions

​

global vlast1 vlast2 beta delta theta k0 kt At p1 p2
hold off
hold all
vlast1=20*ones(1,40);
vlast2=vlast1;
k0=0.4:0.4:16;
kt11=k0;
kt12=k0;
beta=0.98;
delta=0.1;
theta=0.36;
A1=1.75;
p11=0.9;
p12=1-p11;
p21=0.4;
p22=1-p21;
A2=0.75;
numits=250;
for k=1:numits
for j=1:40
kt=k0(j);
At=A1;
p1=p11;
p2=p12;
%we are using a bounded function minimization routine here
z=fminbnd(@valfunsto,0.41,15.99);
v1(j)=-valfunsto(z);
kt11(j)=z;
At=A2;
p1=p21;
p2=p22;
z=fminbnd(@valfunsto,0.41,15.99);
v2(j)=-valfunsto(z);
kt12(j)=z;
end
if k/50==round(k/50)
plot(k0,v1,k0,v2)
drawnow
end
vlast1=v1;
vlast2=v2;
end
hold off
%optimal plotting prgram for the plans
%plot(k0,kt11,k0,kt12)

function val=valfunsto(k)
global vlast1 vlast2 beta delta theta k0 kt At p1 p2
g1=interp1(k0,vlast1,k,’linear’);
g2=interp1(k0,vlast2,k,’linear’);
kk=At*kt^theta-k+(1-delta)*kt;
val=log(kk)+beta*(p1*g1+p2*g2);
val=-val;
end

This codes from the textbook The ABCs of RBCs. Once I run them, the error-
Error using matlab.internal.math.interp1
Input coordinates must be real
keeps coming up. Is there anything wrong with the code?global vlast1 vlast2 beta delta theta k0 kt At p1 p2
hold off
hold all
vlast1=20*ones(1,40);
vlast2=vlast1;
k0=0.4:0.4:16;
kt11=k0;
kt12=k0;
beta=0.98;
delta=0.1;
theta=0.36;
A1=1.75;
p11=0.9;
p12=1-p11;
p21=0.4;
p22=1-p21;
A2=0.75;
numits=250;
for k=1:numits
for j=1:40
kt=k0(j);
At=A1;
p1=p11;
p2=p12;
%we are using a bounded function minimization routine here
z=fminbnd(@valfunsto,0.41,15.99);
v1(j)=-valfunsto(z);
kt11(j)=z;
At=A2;
p1=p21;
p2=p22;
z=fminbnd(@valfunsto,0.41,15.99);
v2(j)=-valfunsto(z);
kt12(j)=z;
end
if k/50==round(k/50)
plot(k0,v1,k0,v2)
drawnow
end
vlast1=v1;
vlast2=v2;
end
hold off
%optimal plotting prgram for the plans
%plot(k0,kt11,k0,kt12)

function val=valfunsto(k)
global vlast1 vlast2 beta delta theta k0 kt At p1 p2
g1=interp1(k0,vlast1,k,’linear’);
g2=interp1(k0,vlast2,k,’linear’);
kk=At*kt^theta-k+(1-delta)*kt;
val=log(kk)+beta*(p1*g1+p2*g2);
val=-val;
end

This codes from the textbook The ABCs of RBCs. Once I run them, the error-
Error using matlab.internal.math.interp1
Input coordinates must be real
keeps coming up. Is there anything wrong with the code? global vlast1 vlast2 beta delta theta k0 kt At p1 p2
hold off
hold all
vlast1=20*ones(1,40);
vlast2=vlast1;
k0=0.4:0.4:16;
kt11=k0;
kt12=k0;
beta=0.98;
delta=0.1;
theta=0.36;
A1=1.75;
p11=0.9;
p12=1-p11;
p21=0.4;
p22=1-p21;
A2=0.75;
numits=250;
for k=1:numits
for j=1:40
kt=k0(j);
At=A1;
p1=p11;
p2=p12;
%we are using a bounded function minimization routine here
z=fminbnd(@valfunsto,0.41,15.99);
v1(j)=-valfunsto(z);
kt11(j)=z;
At=A2;
p1=p21;
p2=p22;
z=fminbnd(@valfunsto,0.41,15.99);
v2(j)=-valfunsto(z);
kt12(j)=z;
end
if k/50==round(k/50)
plot(k0,v1,k0,v2)
drawnow
end
vlast1=v1;
vlast2=v2;
end
hold off
%optimal plotting prgram for the plans
%plot(k0,kt11,k0,kt12)

function val=valfunsto(k)
global vlast1 vlast2 beta delta theta k0 kt At p1 p2
g1=interp1(k0,vlast1,k,’linear’);
g2=interp1(k0,vlast2,k,’linear’);
kk=At*kt^theta-k+(1-delta)*kt;
val=log(kk)+beta*(p1*g1+p2*g2);
val=-val;
end

This codes from the textbook The ABCs of RBCs. Once I run them, the error-
Error using matlab.internal.math.interp1
Input coordinates must be real
keeps coming up. Is there anything wrong with the code? interp1 error MATLAB Answers — New Questions

​

We build a custom hardware board base on AMD rfsoc 27dr, follow the
https://ww2.mathworks.cn/help/soc/ug/create-board-support-framework.html
https://www.youtube.com/watch?v=m5Rfqz2gteE
success build a boardSupportPackage, and install it in mathworks R2023b system, but when apply this custom board to the ‘soc_rfsoc_datacapture’ case, can’t pass configure steps. The simulink report :
Error:A dot ‘.’ indexing expression produced a comma-separated list with 2 values where only a single value is allowed.
I am sure this is ok if we choose the hardware board ‘Xilinx Zynq ultraScale+ RFSoC ZCU111…’, that means, the error is maybe due to our boardSupportPackage.
The attached file is the boardSupportPackage create script.
best regards for reply.We build a custom hardware board base on AMD rfsoc 27dr, follow the
https://ww2.mathworks.cn/help/soc/ug/create-board-support-framework.html
https://www.youtube.com/watch?v=m5Rfqz2gteE
success build a boardSupportPackage, and install it in mathworks R2023b system, but when apply this custom board to the ‘soc_rfsoc_datacapture’ case, can’t pass configure steps. The simulink report :
Error:A dot ‘.’ indexing expression produced a comma-separated list with 2 values where only a single value is allowed.
I am sure this is ok if we choose the hardware board ‘Xilinx Zynq ultraScale+ RFSoC ZCU111…’, that means, the error is maybe due to our boardSupportPackage.
The attached file is the boardSupportPackage create script.
best regards for reply. We build a custom hardware board base on AMD rfsoc 27dr, follow the
https://ww2.mathworks.cn/help/soc/ug/create-board-support-framework.html
https://www.youtube.com/watch?v=m5Rfqz2gteE
success build a boardSupportPackage, and install it in mathworks R2023b system, but when apply this custom board to the ‘soc_rfsoc_datacapture’ case, can’t pass configure steps. The simulink report :
Error:A dot ‘.’ indexing expression produced a comma-separated list with 2 values where only a single value is allowed.
I am sure this is ok if we choose the hardware board ‘Xilinx Zynq ultraScale+ RFSoC ZCU111…’, that means, the error is maybe due to our boardSupportPackage.
The attached file is the boardSupportPackage create script.
best regards for reply. soc blockset, custom board support package, configure error MATLAB Answers — New Questions

​

Hello, and thank you for taking your time to read this message. I just want to understand how does this code work line by line.

R = input(‘Enter radii [R1, R2, R3]: ‘);
theta = input(‘Enter angles [theta1, theta2, theta3] in degrees: ‘);
stopping_criterion = input(‘Enter stopping criterion for Newton-Raphson iterations: ‘);
% Choose solution method
disp(‘Choose a solution method:’);
disp(‘1. Gauss-Elimination with partial pivoting’);
disp(‘2. Cramer Rule’);
disp(‘3. Naïve Gauss-Jordan’);
method = input(‘Enter the method number: ‘);
if method < 1 || method > 3
error(‘Invalid method number’);
end
f = @(params) [params(1)/(1 + params(2) * sind(theta(1) + params(3))) – R(1);
params(1)/(1 + params(2) * sind(theta(2) + params(3))) – R(2);
params(1)/(1 + params(2) * sind(theta(3) + params(3))) – R(3)];
C = 1000;
e = 0.5;
alpha = 30;
relative_errors_C = zeros(100, 1);
relative_errors_e = zeros(100, 1);
relative_errors_alpha = zeros(100, 1);
for iteration = 1:100
J = numerical_jacobian(f, [C, e, alpha]);
if det(J) == 0
error(‘The Jacobian matrix is not invertible. The Newton-Raphson method cannot be used.’);
end
switch method
case 1
update = gauss_elimination_pivoting(J, f([C, e, alpha]));
case 2
update = cramers_rule_solver(J, f([C, e, alpha]));
case 3
update = naive_gauss_jordan(J, f([C, e, alpha]));
end
% Check for convergence
if norm(update) < stopping_criterion
% Update constants
C = C – update(1);
e = e – update(2);
alpha = alpha – update(3);
relative_errors_C(iteration) = abs(update(1) / C);
relative_errors_e(iteration) = abs(update(2) / e);
relative_errors_alpha(iteration) = abs(update(3) / alpha);

fprintf(‘Iteration %d: Relative Error in C = %f, e = %f, alpha = %fn’, iteration, relative_errors_C(iteration), relative_errors_e(iteration), relative_errors_alpha(iteration));
break;
end
end
figure;
subplot(3, 1, 1);
plot(1:iteration, relative_errors_C(1:iteration), ‘o-‘);
xlabel(‘Iteration’);
ylabel(‘Relative Error in C’);
title(‘Convergence’);
subplot(3, 1, 2);
plot(1:iteration, relative_errors_e(1:iteration), ‘o-‘);
xlabel(‘Iteration’);
ylabel(‘Relative Error in e’);
subplot(3, 1, 3);
plot(1:iteration, relative_errors_alpha(1:iteration), ‘o-‘);
xlabel(‘Iteration’);
ylabel(‘Relative Error in alpha’);
fprintf(‘Final values:nC = %fn e = %fn alpha = %fn’, C, e, alpha);
theta_values = linspace(0, 360, 1000);
R_values = C./(1 + e * sind(theta_values + alpha));
figure;
polarplot(deg2rad(theta_values), R_values);
hold on;
polarplot(deg2rad(theta), R, ‘ro’);
title(‘Satellite Orbit’);
legend(‘Orbit’, ‘Data Points’);
hold off;
function J = numerical_jacobian(f, params)
n = length(params);
m = length(f(params));
J = zeros(m, n);
delta = 1e-8;
for i = 1:n
params_plus = params;
params_minus = params;
params_plus(i) = params_plus(i) + delta;
params_minus(i) = params_minus(i) – delta;
J(:, i) = (f(params_plus) – f(params_minus)) / (2 * delta);
end
endHello, and thank you for taking your time to read this message. I just want to understand how does this code work line by line.

R = input(‘Enter radii [R1, R2, R3]: ‘);
theta = input(‘Enter angles [theta1, theta2, theta3] in degrees: ‘);
stopping_criterion = input(‘Enter stopping criterion for Newton-Raphson iterations: ‘);
% Choose solution method
disp(‘Choose a solution method:’);
disp(‘1. Gauss-Elimination with partial pivoting’);
disp(‘2. Cramer Rule’);
disp(‘3. Naïve Gauss-Jordan’);
method = input(‘Enter the method number: ‘);
if method < 1 || method > 3
error(‘Invalid method number’);
end
f = @(params) [params(1)/(1 + params(2) * sind(theta(1) + params(3))) – R(1);
params(1)/(1 + params(2) * sind(theta(2) + params(3))) – R(2);
params(1)/(1 + params(2) * sind(theta(3) + params(3))) – R(3)];
C = 1000;
e = 0.5;
alpha = 30;
relative_errors_C = zeros(100, 1);
relative_errors_e = zeros(100, 1);
relative_errors_alpha = zeros(100, 1);
for iteration = 1:100
J = numerical_jacobian(f, [C, e, alpha]);
if det(J) == 0
error(‘The Jacobian matrix is not invertible. The Newton-Raphson method cannot be used.’);
end
switch method
case 1
update = gauss_elimination_pivoting(J, f([C, e, alpha]));
case 2
update = cramers_rule_solver(J, f([C, e, alpha]));
case 3
update = naive_gauss_jordan(J, f([C, e, alpha]));
end
% Check for convergence
if norm(update) < stopping_criterion
% Update constants
C = C – update(1);
e = e – update(2);
alpha = alpha – update(3);
relative_errors_C(iteration) = abs(update(1) / C);
relative_errors_e(iteration) = abs(update(2) / e);
relative_errors_alpha(iteration) = abs(update(3) / alpha);

fprintf(‘Iteration %d: Relative Error in C = %f, e = %f, alpha = %fn’, iteration, relative_errors_C(iteration), relative_errors_e(iteration), relative_errors_alpha(iteration));
break;
end
end
figure;
subplot(3, 1, 1);
plot(1:iteration, relative_errors_C(1:iteration), ‘o-‘);
xlabel(‘Iteration’);
ylabel(‘Relative Error in C’);
title(‘Convergence’);
subplot(3, 1, 2);
plot(1:iteration, relative_errors_e(1:iteration), ‘o-‘);
xlabel(‘Iteration’);
ylabel(‘Relative Error in e’);
subplot(3, 1, 3);
plot(1:iteration, relative_errors_alpha(1:iteration), ‘o-‘);
xlabel(‘Iteration’);
ylabel(‘Relative Error in alpha’);
fprintf(‘Final values:nC = %fn e = %fn alpha = %fn’, C, e, alpha);
theta_values = linspace(0, 360, 1000);
R_values = C./(1 + e * sind(theta_values + alpha));
figure;
polarplot(deg2rad(theta_values), R_values);
hold on;
polarplot(deg2rad(theta), R, ‘ro’);
title(‘Satellite Orbit’);
legend(‘Orbit’, ‘Data Points’);
hold off;
function J = numerical_jacobian(f, params)
n = length(params);
m = length(f(params));
J = zeros(m, n);
delta = 1e-8;
for i = 1:n
params_plus = params;
params_minus = params;
params_plus(i) = params_plus(i) + delta;
params_minus(i) = params_minus(i) – delta;
J(:, i) = (f(params_plus) – f(params_minus)) / (2 * delta);
end
end Hello, and thank you for taking your time to read this message. I just want to understand how does this code work line by line.

R = input(‘Enter radii [R1, R2, R3]: ‘);
theta = input(‘Enter angles [theta1, theta2, theta3] in degrees: ‘);
stopping_criterion = input(‘Enter stopping criterion for Newton-Raphson iterations: ‘);
% Choose solution method
disp(‘Choose a solution method:’);
disp(‘1. Gauss-Elimination with partial pivoting’);
disp(‘2. Cramer Rule’);
disp(‘3. Naïve Gauss-Jordan’);
method = input(‘Enter the method number: ‘);
if method < 1 || method > 3
error(‘Invalid method number’);
end
f = @(params) [params(1)/(1 + params(2) * sind(theta(1) + params(3))) – R(1);
params(1)/(1 + params(2) * sind(theta(2) + params(3))) – R(2);
params(1)/(1 + params(2) * sind(theta(3) + params(3))) – R(3)];
C = 1000;
e = 0.5;
alpha = 30;
relative_errors_C = zeros(100, 1);
relative_errors_e = zeros(100, 1);
relative_errors_alpha = zeros(100, 1);
for iteration = 1:100
J = numerical_jacobian(f, [C, e, alpha]);
if det(J) == 0
error(‘The Jacobian matrix is not invertible. The Newton-Raphson method cannot be used.’);
end
switch method
case 1
update = gauss_elimination_pivoting(J, f([C, e, alpha]));
case 2
update = cramers_rule_solver(J, f([C, e, alpha]));
case 3
update = naive_gauss_jordan(J, f([C, e, alpha]));
end
% Check for convergence
if norm(update) < stopping_criterion
% Update constants
C = C – update(1);
e = e – update(2);
alpha = alpha – update(3);
relative_errors_C(iteration) = abs(update(1) / C);
relative_errors_e(iteration) = abs(update(2) / e);
relative_errors_alpha(iteration) = abs(update(3) / alpha);

fprintf(‘Iteration %d: Relative Error in C = %f, e = %f, alpha = %fn’, iteration, relative_errors_C(iteration), relative_errors_e(iteration), relative_errors_alpha(iteration));
break;
end
end
figure;
subplot(3, 1, 1);
plot(1:iteration, relative_errors_C(1:iteration), ‘o-‘);
xlabel(‘Iteration’);
ylabel(‘Relative Error in C’);
title(‘Convergence’);
subplot(3, 1, 2);
plot(1:iteration, relative_errors_e(1:iteration), ‘o-‘);
xlabel(‘Iteration’);
ylabel(‘Relative Error in e’);
subplot(3, 1, 3);
plot(1:iteration, relative_errors_alpha(1:iteration), ‘o-‘);
xlabel(‘Iteration’);
ylabel(‘Relative Error in alpha’);
fprintf(‘Final values:nC = %fn e = %fn alpha = %fn’, C, e, alpha);
theta_values = linspace(0, 360, 1000);
R_values = C./(1 + e * sind(theta_values + alpha));
figure;
polarplot(deg2rad(theta_values), R_values);
hold on;
polarplot(deg2rad(theta), R, ‘ro’);
title(‘Satellite Orbit’);
legend(‘Orbit’, ‘Data Points’);
hold off;
function J = numerical_jacobian(f, params)
n = length(params);
m = length(f(params));
J = zeros(m, n);
delta = 1e-8;
for i = 1:n
params_plus = params;
params_minus = params;
params_plus(i) = params_plus(i) + delta;
params_minus(i) = params_minus(i) – delta;
J(:, i) = (f(params_plus) – f(params_minus)) / (2 * delta);
end
end matlab ai chatbot, large language model, llm, matlab playground MATLAB Answers — New Questions

​

I am doing a project on epidemic models. I have derived a formula for basic reproduction number and now I want to analysis its sensitivity to different parameters in the formula. I have come across PRCC (Partial Rank Correlation Coefficient) analysis (which makes use of Latin Hypercube Sampling, I suppose – I am not sure). How can I do this sort of sensitivity analysis using MATLAB?I am doing a project on epidemic models. I have derived a formula for basic reproduction number and now I want to analysis its sensitivity to different parameters in the formula. I have come across PRCC (Partial Rank Correlation Coefficient) analysis (which makes use of Latin Hypercube Sampling, I suppose – I am not sure). How can I do this sort of sensitivity analysis using MATLAB? I am doing a project on epidemic models. I have derived a formula for basic reproduction number and now I want to analysis its sensitivity to different parameters in the formula. I have come across PRCC (Partial Rank Correlation Coefficient) analysis (which makes use of Latin Hypercube Sampling, I suppose – I am not sure). How can I do this sort of sensitivity analysis using MATLAB? partial rank correlation coefficient, latin hypercube sampling, sensitivity analysis MATLAB Answers — New Questions

​

The fuel cell lookup table generated from the spreadsheet via-https://ww2.mathworks.cn/help/autoblks/ug/generate-mapped-fuel-cell-from-a-spreadsheet.html
However, based on the data,the fuel cell efficiency looks always over 60% which is unrealistic.
Is there something wrong from built-in spreadsheet?The fuel cell lookup table generated from the spreadsheet via-https://ww2.mathworks.cn/help/autoblks/ug/generate-mapped-fuel-cell-from-a-spreadsheet.html
However, based on the data,the fuel cell efficiency looks always over 60% which is unrealistic.
Is there something wrong from built-in spreadsheet? The fuel cell lookup table generated from the spreadsheet via-https://ww2.mathworks.cn/help/autoblks/ug/generate-mapped-fuel-cell-from-a-spreadsheet.html
However, based on the data,the fuel cell efficiency looks always over 60% which is unrealistic.
Is there something wrong from built-in spreadsheet? fuel cell, lookup table, efficiency, heat flow MATLAB Answers — New Questions

​