aij

I saw "Introduction to linear dynamical systems" on YouTube, It is much better than what my teacher taught me, that is a little bothered me.

matrix is about linear algebra.

y=Ax

1) y is measurement, x is something we do not know.

2) y is output, x is input.

3) x is signal, y is transformation output.

aij is is weight on xi to produce yj, ith row concerns with ith output, jth column concerns with jth input.

1) aij = 0, then yj does not depend on xi

2) some aij is bigger in i row, y is more depend on xi

3) some aij is bigger in j column, xi more affect yj

4) lower triangular are zeros, yi only depends on x1....xi

5) diagnal, yi only depends on xi.

6) sparsity means relationship between x and y is not so complex.

System Identification

yeah, i am here, jumped too fast!

some commands of matlab:

lsqnonlin--------non-linear least square optimization

lsqcurvefit------use lsqnonlin to fit a curve

pem------------prediction error model

idgrey----------grey box model

idnlgrey--------grey box for non linear model.

ss --------------state space model

sim------------model simulation

bode-----------bode graph

step------------step response

impulse---------impulse response

c2d-------------convert continuous model to discrete model

system identification method:

state space

poles and zeros

frequency method i.e., bode graph

PID control

optimization method:

newton

gauss-newton

steepest gradient

levenberg marquardt