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Scilabヘルプ >> CACSD > Linear System Representation > arma

arma

Scilab armaライブラリ

説明

Armax は,Scilab tlist型'ar'を有するデータを処理します. armacが, Armax Scilabオブジェクトを構築する際に使用されます. 'ar' tlist には,フィールド ['a','b','d','ny','nu','sig']が含まれます.

armac

この間巣は,ArmaxプロセスA(z^-1)y= B(z^-1)u + D(z^-1)sig*e(t) を符号化するScilab tlistを作成します.

-->ar=armac([1,2],[3,4],1,1,1,sig);
-->ar('a')
 ans  =
!   1.    2. !
-->ar('sig')
 ans  =
    1.
armap(ar [,out])

arに関するarmax方程式を表示します.

armap_p(ar [,out])

ar に関するarmax方程式を多項式行列表示で表示します.

[A,B,D]=armap2p(ar)

ar表現から多項式行列を展開します.

armax

n次元ARXプロセスA(z^-1)y= B(z^-1)u + sig*e(t)の係数を 同定するために使用されます.

armax1

armax1 は,1次元 ARX プロセスA(z^-1)y= B(z^-1)u + D(z^-1)sig*e(t) の係数を同定するために使用されます.

arsimul

armax 軌道シミュレーション.

narsimul

armax シミュレーション (rtitrを使用)

odedi

ode および arsimulの簡単なテスト. ODEの 'discret' オプションを試します.

prbs_a

擬似乱数バイナリ列を生成

reglin

線形回帰

// Example extracted from the demo arma3.dem.sce in the cacsd module
// Spectral power estimation
// ( form Sawaragi et all)
m = 18;
a = [1,-1.3136,1.4401,-1.0919,+0.83527];
b = [0.0,0.13137,0.023543,0.10775,0.03516];
u = rand(1,1000,'n');
z = arsimul(a,b,[0],0,u);
//----Using macro mese
[sm,fr]=mese(z,m);
//----The theorical result
function gx=gxx(z, a, b)
  w  = exp(-%i*2*%pi*z*(0:4))'
  gx = abs(b*w)^2/(abs(a*w)^2);
endfunction
res=[];
for x=fr
  res=[ res, gxx(x,a,b)];
end
//----using armax estimation of order  (4,4)
// it's a bit tricky because we are not supposed to know the order
[arc,la,lb,sig,resid]=armax(4,4,z,u);
res1=[];
for x=fr
  res1=[ res1, gxx(x,la(1),lb(1))];
end
//-- visualization of the results
plot2d([fr;fr;fr]',[20*log10(sm/sm(1));20*log10(res/res(1));20*log10(res1/res1(1))]',[2,1,-1])
legend(["Using macro mese";"Theoretical value";"Arma identification"])
xtitle("Spectral power","frequency","spectral estimate")
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