R=[6,-4;-4,14] % Resistance matrix.

R =

     6    -4
    -4    14

V=[9;0], % Voltage vector.

V =

     9
     0

I=inv(R)*V

I =

    1.8529
    0.5294

A=[-4,-4;2,-4], % System matrix of RLC circuit.

A =

    -4    -4
     2    -4

B=[4;0], % Input matrix

B =

     4
     0

C=[0,2], % Output matrix

C =

     0     2

ssRLC=ss(A,B,C,0), % State-space Representation of RLC circuit.
 
a = 
       x1  x2
   x1  -4  -4
   x2   2  -4
 
 
b = 
       u1
   x1   4
   x2   0
 
 
c = 
       x1  x2
   y1   0   2
 
 
d = 
       u1
   y1   0
 
Continuous-time model.
eA=eig(A)

eA =

  -4.0000 + 2.8284i
  -4.0000 - 2.8284i

step(ssRLC)
tfRLC=tf(ssRLC)
 
Transfer function:
      16
--------------
s^2 + 8 s + 24
 
bode(ssRLC)
grid
% Plotting signals.
t=0:0.01:5; % Time vector.
who

Your variables are:

A      C      R      eA     t      
B      I      V      ssRLC  tfRLC  

s=sin(2*t); % Sinusoid with a frequency of 2 rad/sec.
figure
plot(t,s)
e=exp(-0.5*t);
figure
plot(t,e)
size(s)

ans =

     1   501

size(e)

ans =

     1   501

ds=s.*e;
figure
plot(t,ds)
title('Damped Sinusoid')
ylabel('Output Response')
xlabel('Time (seconds)')
figure(3)
title('Exponential Decay')
uiopen('Z:\mlab\340\demos\stp_fn.m',1)
x=2*stp_fn(t)-2.5*stp_fn(t-1.5)+0.5*stp_fn(t-4);
figure
plot(t,x)
axis([-1 6 -1 3])
help eig
 EIG    Eigenvalues and eigenvectors.
    E = EIG(X) is a vector containing the eigenvalues of a square 
    matrix X.
 
    [V,D] = EIG(X) produces a diagonal matrix D of eigenvalues and a
    full matrix V whose columns are the corresponding eigenvectors so
    that X*V = V*D.
 
    [V,D] = EIG(X,'nobalance') performs the computation with balancing
    disabled, which sometimes gives more accurate results for certain
    problems with unusual scaling. If X is symmetric, EIG(X,'nobalance')
    is ignored since X is already balanced.
 
    E = EIG(A,B) is a vector containing the generalized eigenvalues
    of square matrices A and B.
 
    [V,D] = EIG(A,B) produces a diagonal matrix D of generalized
    eigenvalues and a full matrix V whose columns are the
    corresponding eigenvectors so that A*V = B*V*D.
 
    EIG(A,B,'chol') is the same as EIG(A,B) for symmetric A and symmetric
    positive definite B.  It computes the generalized eigenvalues of A and B
    using the Cholesky factorization of B.
    EIG(A,B,'qz') ignores the symmetry of A and B and uses the QZ algorithm.
    In general, the two algorithms return the same result, however using the
    QZ algorithm may be more stable for certain problems.
    The flag is ignored when A and B are not symmetric.
 
    See also <a href="matlab:help condeig">condeig</a>, <a href="matlab:help eigs">eigs</a>, <a href="matlab:help ordeig">ordeig</a>.

    Overloaded functions or methods (ones with the same name in other directories)
       <a href="matlab:help lti/eig.m">help lti/eig.m</a>
       <a href="matlab:help sym/eig.m">help sym/eig.m</a>

    Reference page in Help browser
       <a href="matlab:doc eig">doc eig</a>



edit
xx=dummy(t);
check=xx-t;
plot(check)
diary off