I want to implement a Moving AVerage model of lag 2 whose functional form is :
y[n] = h1*x[n] + h2*x[n-1] + h3*x[n-2]
having coefficients, h_true = [h1, h2, h3];
The output is a scalar valued number at n.
Matlab has filter() function which can be used to implement a MA or AR model. But, the output is different when I implemented the equation as it is and when using the filter() function. What is the correct way? Please find below the code.
N = 10;
x = rand(1,N);
h_true = [1, 0.6, 0.3]; %h1 = 1, h2 = 0.6; h3 = 0.3
y(1) = 0.0;
y(2) = 0.0;
for i =3 : N
y(i) = h_true(1)*x(i) + h_true(2)*x(i-1) + h_true(3)*x(i-2);
end
filtered_y = filter(h_true,1,x);
y and filtered_y is different
While some of the terms do vanish for i<3, not all terms actually do. So as you compute y, you should still be accounting for those non vanishing terms:
y(1) = h_true(1)*x(1);
y(2) = h_true(1)*x(2) + h_true(2)*x(1);
for i =3 : N
y(i) = h_true(1)*x(i) + h_true(2)*x(i-1) + h_true(3)*x(i-2);
end
Related
h=0.005;
x = 0:h:40;
y = zeros(1,length(x));
y(1) = 0;
F_xy = ;
for i=1:(length(x)-1)
k_1 = F_xy(x(i),y(i));
k_2 = F_xy(x(i)+0.5*h,y(i)+0.5*h*k_1);
k_3 = F_xy((x(i)+0.5*h),(y(i)+0.5*h*k_2));
k_4 = F_xy((x(i)+h),(y(i)+k_3*h));
y(i+1) = y(i) + (1/6)*(k_1+2*k_2+2*k_3+k_4)*h;
end
I have the following code, I think it's right. I know there's parts missing on the F_xy because this is my follow up question.
I have dx/dt = = −x(2 − y) with t_0 = 0, x(t_0) = 1
and dy/dt = y(1 − 2x) with t_0 = 0, y(t_0) = 2.
My question is that I don't know how to get these equations in to the code. All help appreciated
You are using both t and x as independent variable in an inconsistent manner. Going from the actual differential equations, the independent variable is t, while the dependent variables of the 2-dimensional system are x and y. They can be combined into a state vector u=[x,y] Then one way to encode the system close to what you wrote is
h=0.005;
t = 0:h:40;
u0 = [1, 2]
u = [ u0 ]
function udot = F(t,u)
x = u(1); y = u(2);
udot = [ -x*(2 - y), y*(1 - 2*x) ]
end
for i=1:(length(t)-1)
k_1 = F(t(i) , u(i,:) );
k_2 = F(t(i)+0.5*h, u(i,:)+0.5*h*k_1);
k_3 = F(t(i)+0.5*h, u(i,:)+0.5*h*k_2);
k_4 = F(t(i)+ h, u(i,:)+ h*k_3);
u(i+1,:) = u(i,:) + (h/6)*(k_1+2*k_2+2*k_3+k_4);
end
with a solution output
Is F_xy your derivative function?
If so, simply write it as a helper function or function handle. For example,
F_xy=#(x,y)[-x*(2-y);y*(1-2*x)];
Also note that your k_1, k_2, k_3, k_4, y(i) are all two-dimensional. You need to re-size your y and rewrite the indices in your iterating steps accordingly.
Introduction
I am using Matlab to simulate some dynamic systems through numerically solving systems of Second Order Ordinary Differential Equations using ODE45. I found a great tutorial from Mathworks (link for tutorial at end) on how to do this.
In the tutorial the system of equations is explicit in x and y as shown below:
x''=-D(y) * x' * sqrt(x'^2 + y'^2)
y''=-D(y) * y' * sqrt(x'^2 + y'^2) + g(y)
Both equations above have form y'' = f(x, x', y, y')
Question
However, I am coming across systems of equations where the variables can not be solved for explicitly as shown in the example. For example one of the systems has the following set of 3 second order ordinary differential equations:
y double prime equation
y'' - .5*L*(x''*sin(x) + x'^2*cos(x) + (k/m)*y - g = 0
x double prime equation
.33*L^2*x'' - .5*L*y''sin(x) - .33*L^2*C*cos(x) + .5*g*L*sin(x) = 0
A single prime is first derivative
A double prime is second derivative
L, g, m, k, and C are given parameters.
How can Matlab be used to numerically solve a set of second order ordinary differential equations where second order can not be explicitly solved for?
Thanks!
Your second system has the form
a11*x'' + a12*y'' = f1(x,y,x',y')
a21*x'' + a22*y'' = f2(x,y,x',y')
which you can solve as a linear system
[x'', y''] = A\f
or in this case explicitly using Cramer's rule
x'' = ( a22*f1 - a12*f2 ) / (a11*a22 - a12*a21)
y'' accordingly.
I would strongly recommend leaving the intermediate variables in the code to reduce chances for typing errors and avoid multiple computation of the same expressions.
Code could look like this (untested)
function dz = odefunc(t,z)
x=z(1); dx=z(2); y=z(3); dy=z(4);
A = [ [-.5*L*sin(x), 1] ; [.33*L^2, -0.5*L*sin(x)] ]
b = [ [dx^2*cos(x) + (k/m)*y-g]; [-.33*L^2*C*cos(x) + .5*g*L*sin(x)] ]
d2 = A\b
dz = [ dx, d2(1), dy, d2(2) ]
end
Yes your method is correct!
I post the following code below:
%Rotating Pendulum Sym Main
clc
clear all;
%Define parameters
global M K L g C;
M = 1;
K = 25.6;
L = 1;
C = 1;
g = 9.8;
% define initial values for theta, thetad, del, deld
e_0 = 1;
ed_0 = 0;
theta_0 = 0;
thetad_0 = .5;
initialValues = [e_0, ed_0, theta_0, thetad_0];
% Set a timespan
t_initial = 0;
t_final = 36;
dt = .01;
N = (t_final - t_initial)/dt;
timeSpan = linspace(t_final, t_initial, N);
% Run ode45 to get z (theta, thetad, del, deld)
[t, z] = ode45(#RotSpngHndl, timeSpan, initialValues);
%initialize variables
e = zeros(N,1);
ed = zeros(N,1);
theta = zeros(N,1);
thetad = zeros(N,1);
T = zeros(N,1);
V = zeros(N,1);
x = zeros(N,1);
y = zeros(N,1);
for i = 1:N
e(i) = z(i, 1);
ed(i) = z(i, 2);
theta(i) = z(i, 3);
thetad(i) = z(i, 4);
T(i) = .5*M*(ed(i)^2 + (1/3)*L^2*C*sin(theta(i)) + (1/3)*L^2*thetad(i)^2 - L*ed(i)*thetad(i)*sin(theta(i)));
V(i) = -M*g*(e(i) + .5*L*cos(theta(i)));
E(i) = T(i) + V(i);
end
figure(1)
plot(t, T,'r');
hold on;
plot(t, V,'b');
plot(t,E,'y');
title('Energy');
xlabel('time(sec)');
legend('Kinetic Energy', 'Potential Energy', 'Total Energy');
Here is function handle file for ode45:
function dz = RotSpngHndl(~, z)
% Define Global Parameters
global M K L g C
A = [1, -.5*L*sin(z(3));
-.5*L*sin(z(3)), (1/3)*L^2];
b = [.5*L*z(4)^2*cos(z(3)) - (K/M)*z(1) + g;
(1/3)*L^2*C*cos(z(3)) + .5*g*L*sin(z(3))];
X = A\b;
% return column vector [ed; edd; ed; edd]
dz = [z(2);
X(1);
z(4);
X(2)];
In Matlab I want to create the partial derivative of a cost function called J(theta_0, theta_1) (in order to do the calculations necessary to do gradient descent).
The function J(theta_0, theta_1) is defined as:
Lets say h_theta(x) = theta_1 + theta_2*x. Also: alpha is fixed, the starting values of theta_1 and theta_2 are given. Let's say in this example: alpha = 0.1 theta_1 = 0, theta_2 = 1. Also I have all the values for x and y in two different vectors.
VectorOfX =
5
5
6
VectorOfX =
6
6
10
Steps I took to try to solve this in Matlab: I have no clue how to solve this problem in matlab. So I started off with trying to define a function in Matlab and tried this:
theta_1 = 0
theta_2 = 1
syms x;
h_theta(x) = theta_1 + t2*x;
This worked, but is not what I really wanted. I wanted to get x^(i), which is in a vector. The next thing I tried was:
theta_1 = 0
theta_2 = 1
syms x;
h_theta(x) = theta_1 + t2*vectorOfX(1);
This gives the following error:
Error using sym/subsindex (line 672)
Invalid indexing or function definition. When defining a
function, ensure that the body of the function is a SYM
object. When indexing, the input must be numeric, logical or
':'.
Error in prog1>gradientDescent (line 46)
h_theta(x) = theta_1 + theta_2*vectorOfX(x);
I looked up this error and don't know how to solve it for this particular example. I have the feeling that I make matlab work against me instead of using it in my favor.
When I have to perform symbolic computations I prefer to use Mathematica. In that environment this is the code to get the partial derivatives you are looking for.
J[th1_, th2_, m_] := Sum[(th1 + th2*Subscript[x, i] - Subscript[y, i])^2, {i, 1, m}]/(2*m)
D[J[th1, th2, m], th1]
D[J[th1, th2, m], th2]
and gives
Coming back to MATLAB we can solve this problem with the following code
%// Constants.
alpha = 0.1;
theta_1 = 0;
theta_2 = 1;
X = [5 ; 5 ; 6];
Y = [6 ; 6 ; 10];
%// Number of points.
m = length(X);
%// Partial derivatives.
Dtheta1 = #(theta_1, theta_2) sum(2*(theta_1+theta_2*X-Y))/2/m;
Dtheta2 = #(theta_1, theta_2) sum(2*X.*(theta_1+theta_2*X-Y))/2/m;
%// Loop initialization.
toll = 1e-5;
maxIter = 100;
it = 0;
err = 1;
theta_1_Last = theta_1;
theta_2_Last = theta_2;
%// Iterations.
while err>toll && it<maxIter
theta_1 = theta_1 - alpha*Dtheta1(theta_1, theta_2);
theta_2 = theta_2 - alpha*Dtheta2(theta_1, theta_2);
it = it + 1;
err = norm([theta_1-theta_1_Last ; theta_2-theta_2_Last]);
theta_1_Last = theta_1;
theta_2_Last = theta_2;
end
Unfortunately for this case the iterations does not converge.
MATLAB is not very flexible for symbolic computations, however a way to get those partial derivatives is the following
m = 10;
syms th1 th2
x = sym('x', [m 1]);
y = sym('y', [m 1]);
J = #(th1, th2) sum((th1+th2.*x-y).^2)/2/m;
diff(J, th1)
diff(J, th2)
I need to write a for loop in matlab to solve a derivative using the forward difference method. The function to derive is 10+15x+20x^2 from 0 to 10 using steps of 0.25. I have tried using
h=.25;
x=[0:h:10];
y = 10+15*x+20*x.^2;
y(1) = 45; size(x)
for i=2:47,
y(i) = y(i-1) + h*(15+40*x);
end
I'd do like this, as a start,
h=.25;
x=[0:h:10];
y = 10+15*x+20*x.^2;
diff(y)./diff(x)
or, as alternative,
syms x;
y = 20.*x.^2 + 15.*x + 10;
dy = diff(y,1);
h=.25;
xx=[0:h:10];
res = subs(dy,xx);
If I'm to integrate a function
y = -((F+h)M^3(cosh(h*M)+M*beta*sinh(h*M)))/(h*M*cosh(h*M)+(-1+h*M^2*beta)*sinh(h*M))- (alpha*(M^2*(F+h)*(-1+2*h^2*M^2+ cosh(2*h*M)-2*h*M*sinh(2*h*M)))/(8*(h*M*cosh(h*M)+(-1+h*M^2*beta)*sinh(h*M))^2));
with respect to x, where
phi = 0.6;
x = 0.5;
M = 2;
theta = -1:0.5:1.5;
F = theta - 1;
h = 1 + phi*cos(2*pi*x);
alpha = 0.2;beta = 0.0;
I have written an Mfile
function r = parameterIntegrate(F,h,M,beta,alpha,theta,phi)
% defining a nested function that uses one variable
phi = 0.6;
x = 0.5;
r = quad(#testf,0,1 + phi*cos(2*pi*x));
% simpson's rule from 0 to h
function y = testf(x)
h = 1 + phi*cos(2*pi*x);
theta = -1:0.5:1.5;
F = theta - 1;
M = 2;
beta = 0;
alpha = 0;
y = -((F+h)*M^3*(cosh(h*M)+M*beta*sinh(h*M)))/(h*M*cosh(h*M)+(-1+h*M^2*beta)*sinh(h*M))- (alpha*(M^2*(F+h)*(-1+2*h^2*M^2+ cosh(2*h*M)-2*h*M*sinh(2*h*M)))/(8*(h*M*cosh(h*M)+(-1+h*M^2*beta)*sinh(h*M))^2));
end
end
and called the function by
tol = [1e-5 1e-3];
q = quad(#parameterIntegrate, 0, h,tol)
or
q = quad(#parameterIntegrate, 0,1 + phi*cos(2*pi*0.5),tol)
its not working its giving me
Error using ==> plus
Matrix dimensions must agree.
What your error message means is that for some line of code, there are 2 matrices, but the dimensions don't match, so it can't add them. What I suggest you do to solve this is as follows:
Figure out exactly which line of code is causing the problem.
If the line has large numbers of variables, simplify them some.
Remember that if there are any matrixs at all, and you don't want to do matrix multiplication/division, use the .*, ./, and .^.
I suspect that if you change your multiplies/divides with step 3, your problem will go away.