统计智能信号处理的matlab源程序,也不知道网站里有没有。发上来请站长审阅吧

				function [a,b,V,FPE,C]=armals(x,p,q,maxiter,tol)
				% function [a,b,V,FPE,C]=armals(x,p,q,maxiter,tol)
				% ARMA modeling using non-linear LS optimization
				%  
				% Programmed by: Dimitris Manolakis
				% Revised: 7/1/94
				%
				%-----------------------------------------------------------
				% Copyright 2000, by Dimitris G. Manolakis, Vinay K. Ingle,
				% and Stephen M. Kogon.  For use with the book
				% "Statistical and Adaptive Signal Processing"
				% McGraw-Hill Higher Education.
				%-----------------------------------------------------------
				
				
				Lx=length(x);
				pqmax=max([p q]);
				
				%tol=0.01;maxiter=10;
				
				% Compute initial AR(p+q) estimate, residuals,
				% and cost function
				 
				[apq,ear,Vpq,FPEpq]=arls(x,p+q);
				
				% Compute initial ARMA(p,q) parameters using
				% equation error LS with input ear and output x
				
				[a,b,V,FPE]=pzls(x,ear,p,q);
				
				% Convert b into its minimum phase equivalent
				% and then compute the prediction error
				
				b=miphapol(b); b=b(:);
				e=pefilter(a,b,x,zeros(pqmax,1));
				V2=(e'*e)/(Lx-pqmax);
				V=V2;
				
				
				% Start iterative minimization
				%====================================================
				
				% Initialization
				%----------------------------------------------------
				
				g=ones(p+q,1);
				l=0;
				st=0;
				
				% Minimization loop
				%----------------------------------------------------
				while [norm(g)>tol l				
				l=l+1;
				
				% Compute gradient
				%----------------------------------------------------
				
				yf=filter(-1,b,x);
				ef=filter(1,b,e);
				
				R=zeros(p+q,p+q);
				F=zeros(p+q,1);
				
				for n=pqmax+1:Lx
				  psi=zeros(p+q,1);
				  for k=1:p, psi(k)=yf(n-k); end
				  for k=1:q, psi(k+p,:)=ef(n-k); end
				  R=R+psi*psi'; F=F+psi*e(n);
				end
				
				g=R\F;
				
				% Search along gradient direction
				%----------------------------------------------------
				
				[a1,b1,e,V1,st]=gstep(x,a,b,g,V,p,q); 
				
				a=a1;
				b=b1;
				V=V1;
				
				end
				
				% End iterative minimization
				%====================================================
				
				V=(e'*e)/(Lx-pqmax);
				
				FPE=V*(1+(p+q)/Lx)/(1-(p+q)/Lx);
				
				% Covariance error matrix
				
				C=V*inv(R);