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Scilab help >> Signal Processing > rpem

# rpem

Recursive Prediction-Error Minimization estimation

### Calling Sequence

`[w1,[v]]=rpem(w0,u0,y0,[lambda,[k,[c]]])`

### Arguments

w0

`list(theta,p,l,phi,psi)` where:

theta

[a,b,c] is a real vector of order `3*n`

a,b,c

`a=[a(1),...,a(n)], b=[b(1),...,b(n)], c=[c(1),...,c(n)]`

p

(3*n x 3*n) real matrix.

phi,psi,l

real vector of dimension `3*n`

Applicable values for the first call:

`theta=phi=psi=l=0*ones(1,3*n). p=eye(3*n,3*n)`
u0

real vector of inputs (arbitrary size). (`u0(\$)` is not used by rpem)

y0

vector of outputs (same dimension as `u0`). (`y0(1)` is not used by rpem).

If the time domain is `(t0,t0+k-1)` the `u0` vector contains the inputs

`u(t0),u(t0+1),..,u(t0+k-1)` and `y0` the outputs

`y(t0),y(t0+1),..,y(t0+k-1)`

### Optional arguments

lambda

optional argument (forgetting constant) choosed close to 1 as convergence occur:

`lambda=[lambda0,alfa,beta]` evolves according to :

`lambda(t)=alfa*lambda(t-1)+beta`

with `lambda(0)=lambda0`

k

contraction factor to be chosen close to 1 as convergence occurs.

`k=[k0,mu,nu]` evolves according to:

`k(t)=mu*k(t-1)+nu`

with `k(0)=k0`.

c

Large argument.(`c=1000` is the default value).

### Outputs:

w1

Update for `w0`.

v

sum of squared prediction errors on `u0, y0`.(optional).

In particular `w1(1)` is the new estimate of `theta`. If a new sample `u1, y1` is available the update is obtained by:

`[w2,[v]]=rpem(w1,u1,y1,[lambda,[k,[c]]])`. Arbitrary large series can thus be treated.

### Description

Recursive estimation of arguments in an ARMAX model. Uses Ljung-Soderstrom recursive prediction error method. Model considered is the following:

```y(t)+a(1)*y(t-1)+...+a(n)*y(t-n)=
b(1)*u(t-1)+...+b(n)*u(t-n)+e(t)+c(1)*e(t-1)+...+c(n)*e(t-n)```

The effect of this command is to update the estimation of unknown argument `theta=[a,b,c]` with

`a=[a(1),...,a(n)], b=[b(1),...,b(n)], c=[c(1),...,c(n)]`.

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