lecture 4: parameter estimation methods 1: prediction...

Lecture 4: Parameter Estimation

Methods 1

Sahar Moghimi

System Identification

We have a set of models (based on the parameter vector)

…and a set of measurements

2

3

Prediction error minimization

State space

Probabilistic approach

Prediction error identification methods

Approach: choose parameters to make the PE as small as

possible

In order to define a scalar measure:

Linear filter: if set properly

can reduce the effect of noise

Time varying norm:

Weighted norm: 4

Least squares method

Special case of prediction error methods:

5

On the consistency of LSE

6

7

Weighted LES: to value different measurements in

a different fashion:

Dealing with the issue of best regressors

8

Weighted Principle Component Regression (PCR) Consider

The number of columns (p+1) in X should be sufficiently large to cover the whole duration of the impulse response function which can approximate the system relatively accurately.

If p+1 is greater than the number of nonzero samples in the true impulse response, then the true value of the corresponding extraneous elements in A should be zero.

Assumed uncorrelated

9

We consider decomposing X into its principal components and solving the

linear regression problem in the domain of PCs using Singular Value

Decomposition (SVD).

Treat as black box: code widely available

In MATLAB: [U,D,V]=svd(X,0)

T

1

00

00

00

VU

nd

d

X

Dealing with the issue of best regressors

The di are called the singular values of X

If D is singular, some of the di will be 0

In general rank(X) = number of nonzero di

The column vectors of U (principal components) are the eigenvectors of

the matrix XXT.

The column vectors of V are the eigenvectors of the matrix XTX, and

D is a diagonal matrix with its diagonal elements (singular values) being the

square root of the eigenvalues of XTX (or XXT).

Dealing with the issue of best regressors

Why is SVD so useful?

Application #1: inverses

A-1=(VT)-1 W-1

U-1 = V W-1 UT

Using fact that inverse = transpose

for orthogonal matrices

Since W is diagonal, W-1 also diagonal with reciprocals of

entries of W

Dealing with the issue of best regressors

A-1=(VT)-1 W-1

U-1 = V W-1 UT

This fails when some wi are 0

It’s supposed to fail – singular matrix

Pseudoinverse: if wi=0, set 1/wi to 0 (!)

“Closest” matrix to inverse

Defined for all (even non-square, singular, etc.) matrices

Equal to (ATA)-1AT if ATA invertible

Dealing with the issue of best regressors

Solving Ax=b by least squares

x=pseudoinverse(A) times b

Compute pseudoinverse using SVD

Lets you see if data is singular

Even if not singular, ratio of max to min singular values

(condition number) tells you how stable the solution will be

Set 1/wi to 0 if wi is small (even if not exactly 0)

Dealing with the issue of best regressors

14

If some of the diagonal elements of D are small, it means that the corresponding

PCs have small variances and consequently less information.

We rank the PCs in a descending order according to their singular values in

evaluating their contributions to the output.

We form a series of matrices U each containing a subset of PCs by adding one PC

at a time as a column vector.

The matrices U differ by the number of columns involved and their associated

regression equations (Y = UB + E ) represent the candidate models from which the

"best" model should be selected.

Note that these candidate models are data-specific.

For model selection, we employ the widely used model order selection criteria.

Dealing with the issue of best regressors

Dealing with the issue of best regressors

15

With q PCs and the corresponding U, V and D matrices

denoted as 𝑈 (𝑁 × 𝑞), 𝑉 (𝑝 × 𝑞) and 𝐷 (𝑞 × 𝑞)

Estimating State space models: Subspace

method

16

Estimating the state space model using LSE

If we do not have an insight into the particular structure,

for different state vectors, we can have an indefinite

number of solutions

17

If we knew the sequence of state vectors:

Therefore the state and output can be estimated

using LS.

Estimating State space models: Subspace

method

Estimating State space models: Subspace

method

18

If we define

Estimating State space models: Subspace

method

19

K step ahead prediction based on a finite number of

measurements:

Algorithm steps:

20

Instrumental variable method

21

22

23

24

25

26

27 Slides 14-21 from: http://www.it.uu.se/edu/course/homepage/systemid/vt05

28

Example

29

Example:

30

31

Exercise:

Implement the LSE and IV methods on your data.

Analyze the estimation error and discuss your findings