自适应matlab程序（LMS和RLS）

				自适应matlab程序（LMS和RLS）
				
				 
				%lms算法源程序
				clear all
				close all
				%channel system order
				sysorder = 5 ;
				% Number of system points
				N=2000;
				inp = randn(N,1);
				n = randn(N,1);
				[b,a] = butter(2,0.25);
				Gz = tf(b,a,-1);
				%This function is submitted to make inverse Z-transform (Matlab central file exchange)
				%The first sysorder weight value
				%h=ldiv(b,a,sysorder)';
				% if you use ldiv this will give h :filter weights to be
				h= [0.0976;
				0.2873;
				0.3360;
				0.2210;
				0.0964;];
				y = lsim(Gz,inp);
				%add some noise
				n = n * std(y)/(10*std(n));
				d = y + n;
				totallength=size(d,1);
				%Take 60 points for training
				N=60 ; 
				%begin of algorithm
				w = zeros ( sysorder , 1 ) ;
				for n = sysorder : N 
				u = inp(n:-1:n-sysorder+1) ;
				y(n)= w' * u;
				e(n) = d(n) - y(n) ;
				% Start with big mu for speeding the convergence then slow down to reach the correct weights
				if n < 20
				mu=0.32;
				else
				mu=0.15;
				end
				w = w + mu * u * e(n) ;
				end 
				%check of results
				for n = N+1 : totallength
				u = inp(n:-1:n-sysorder+1) ;
				y(n) = w' * u ;
				e(n) = d(n) - y(n) ;
				end 
				hold on
				plot(d)
				plot(y,'r');
				title('System output') ;
				xlabel('Samples')
				ylabel('True and estimated output')
				figure
				semilogy((abs(e))) ;
				title('Error curve') ;
				xlabel('Samples')
				ylabel('Error value')
				figure
				plot(h, 'k+')
				hold on
				plot(w, 'r*')
				legend('Actual weights','Estimated weights')
				title('Comparison of the actual weights and the estimated weights') ;
				axis([0 6 0.05 0.35]) 
				% RLS 算法
				randn('seed', 0) ;
				rand('seed', 0) ;
				NoOfData = 8000 ; % Set no of data points used for training
				Order = 32 ; % Set the adaptive filter order
				Lambda = 0.98 ; % Set the forgetting factor
				Delta = 0.001 ; % R initialized to Delta*I
				x = randn(NoOfData, 1) ;% Input assumed to be white
				h = rand(Order, 1) ; % System picked randomly
				d = filter(h, 1, x) ; % Generate output (desired signal)
				% Initialize RLS
				P = Delta * eye ( Order, Order ) ;
				w = zeros ( Order, 1 ) ;
				% RLS Adaptation
				for n = Order : NoOfData ;
				u = x(n:-1:n-Order+1) ;
				pi_ = u' * P ;
				k = Lambda + pi_ * u ;
				K = pi_'/k;
				e(n) = d(n) - w' * u ;
				w = w + K * e(n) ;
				PPrime = K * pi_ ;
				P = ( P - PPrime ) / Lambda ;
				w_err(n) = norm(h - w) ;
				end ;
				% Plot results
				figure ;
				plot(20*log10(abs(e))) ;
				title('Learning Curve') ;
				xlabel('Iteration Number') ;
				ylabel('Output Estimation Error in dB') ;
				figure ;
				semilogy(w_err) ;
				title('Weight Estimation Error') ;
				xlabel('Iteration Number') ;
				ylabel('Weight Error in dB') ;