MATLAB Online Course
Learn the basics of MATLAB® through this introductory tutorial on commonly used features and workflows.
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MATLAB Examples of Numerical Programming
by Y.-K. Kim
Example codes using MATLAB built-in functions
You can download matlab code
Non-linear solver
First, define a non-linear function in the form of f(v)=0.
​
Copy v0= 0.5 ;
v=fzero(@fnSolarCell,v0)
function y= fnSolarCell (x)
q= 1.6022E-19 ; k= 1.3806E-23 ; Voc= 0.5 ; T= 297 ;
qkT=q/(k*T);
y=exp(qkT*x)*( 1 +qkT*x)-exp(qkT*Voc);
end
Integration
Integrating discrete dataset: trapz()
Trapezoidal Method
Copy % Discrete dataset
x=[ 0 5 10 15 20 25 30 35 40 45 50 55 60 ]
y=[ 0 3 8 20 33 42 40 48 60 12 8 4 3 ]
plot(x,y)
% Matlab function
I_matlab = trapz(x,y);
Integrating a Function: integral(fun, a,b)
The area of the shaded region shown in the figure can be calculated by:
Copy a=- 3 ; b= 3 ; N= 8 ;
h=(b-a)/N;
x=a:h:b;
fun=@(x) ( 1 -x.^2).^0.5;
% Matlab function
I_matlab = integral(fun,a,b);
Differentiation
Differentiating discrete dataset
Copy % Differentiation from discrete data
X = [ 1 1 2 3 5 8 13 21 ];
Y = diff(X)
% Differentiation from discrete data
h = 0.001 ; % step size
X = - pi :h: pi ; % domain
f = sin(X); % range
Y = diff(f)/h; % first derivative
Z = diff(Y)/h; % second derivative
plot(X(:, 1 :length(Y)),Y, 'r' ,X,f, 'b' , X(:, 1 :length(Z)),Z, 'k' )
Differentiate a Function
Copy % Ordinary Differentiation of a function
syms x
g = exp(x)*cos(x);
diff(g)
% 2nd order Differentiation of a function
diff(g, 2 )
% Partial Differentiation of a function
syms s t
f = sin(s*t);
diff(f,t)
Linear Equations
Solve for Ax=b
Copy % A, b
A=[9.5, -2.5, 0, -2, 0; -2.5, 11, -3.5, 0, -5; 0,-3.5, 15.5, 0, -4; -2, 0, 0, 7, -3; 0, -5, -4, -3, 12];
b=[ 12 ; - 16 ; 14 ; 10 ; - 30 ];
% solve for Ax=b
x=A\b
x=inv(A)*b
% condition number
c=cond(A)
% norm
n=norm(A)
% eigenvalue/vector
[eigVec,eigVa]=eig(A)
% QR factorization
[Q,R]=qr(A)
% LU factorization
[L,U]=lu(A)
Polynomial fitting
Copy t = 1 : 1 : 15 ;
V=[ 2.272 2.092 1.887 1.629 1.482 1.308 1.030 0.875 0.693 0.470 0.336 0.095 - 0.163 - 0.371 - 0.511 ];
% Matlab function for polynomial fit
Zopt=polyfit(t,V, 1 );
Yopt=polyval(Zopt,t); % Matlab function
figure
plot(t,V, '*r' )
hold on
plot(t,Yopt, '-b' )
xlabel( 'time' , 'fontsize' , 15 )
ylabel( 'V' , 'fontsize' , 15 )
Exponential Fitting
RC circuit with unknown capacitor C and resistor of 5M
a) Find the capacitance C from curve fitting b) Estimate the voltage when time=32sec
Copy Xdata = 1 : 1 : 15 ;
Ydata = [ 9.7 8.1 6.6 5.1 4.4 3.7 2.8 2.4 2.0 1.6 1.4 1.1 0.85 0.69 0.6 ];
% Matlab function
Zopt=polyfit(Xdata,log(Ydata), 1 )
R= 5e6 ;
a0=Zopt( 2 );
a1=Zopt( 1 );
V0=exp(a0);
tau=- 1 /a1;
C=tau/R;
% Exponential model
Yopt=V0*exp(- 1 /(R*C).*Xdata);
figure
plot(Xdata,Ydata, '*r' )
hold on
plot(Xdata,Yopt)
hold on
xlabel( 'time (s)' , 'fontsize' , 15 )
ylabel( 'V_R(volt)' , 'fontsize' , 15 )
1st order ODE-IVP
Solve for the response of an RC circuit with a DC input. Let tau= 4.9919; Vm=11.91;
Copy % Initial Condition
a= 0 ; b= 15 ; h= 1 ;
y0 = 11.91 ;
t=a:h:b;
%% MATLAB's function ODE45
[tmat,vmat] = ode45(@myRC, [a b], y0); % Fourth/Fifth RK
figure()
plot(tmat,vmat, '.b' )
xlabel( 'time (s)' , 'fontsize' , 15 )
ylabel( 'V_R(volt)' , 'fontsize' , 15 )
function dvdx = myRC (v)
tau= 4.9919 ; T= 1 /tau; Vm= 11.91 ;
dvdx =-T*v + 1 *T*Vm;
end
2nd order ODE - IVP
Solve an m-c-k system with a sinusoidal input.
Use m=10kg ; k=800 N/m; c=200 N/(m/s), f=10Hz , h=0.01, Fdc=100N.
Copy % Initial Condition
y0 = 0 ; v0 = 0 ;
Yinit = [y0 v0];
a= 0 ; b= 1 ; h= 0.01 ;
tspan = [a:h:b];
% MATLAB's function ODE45
[Time Y] = ode45(@mckFunc,tspan,Yinit);
figure
subplot( 2 , 1 , 1 )
plot(Time,Y(:, 1 ), '--b' )
xlabel( 'Time (s)' )
ylabel( 'Position (m)' )
title( 'ode45' )
subplot( 2 , 1 , 2 )
plot(Time,Y(:, 2 ), 'k' )
xlabel( 'Time (s)' )
ylabel( 'Velocity (m/s)' )
function [ dXdt ] = mckFunc (t,x)
dXdt=zeros( 2 , 1 ); % column vector
m= 10 ; k= 800 ; c= 200 ; f= 10 ;
FinDC= 100 ;
Fin=FinDC*cos( 2 * pi *f*t);
dXdt( 1 )=x( 2 );
dXdt( 2 )= 1 /m*(Fin-c*x( 2 )-k*x( 1 ));
end
Eigenvalue
What are the eigenvalues for a given m-c-k system response? Use m=10kg ; k=800 N/m; c=200 N/(m/s)
Copy k= 800 ; c= 200 ; m= 10 ;
A = [ 0 1 ; -k/m, -c/m];
disp( 'Eigvalue and vector of A (MATLAB):' );
[eigVec,eigVa]=eig(A)
More tutorial codes