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See the recommended documentation of this function

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)].