RLS算法在MATLAB上仿真实现的程序？

chanfu

RLS算法在MATLAB上仿真实现的程序？

twel2e

% RLS 算法
8 J6 m  q8 H- O+ k8 Z; D( c* t- c<br>randn('seed', 0) ;
<br>rand('seed', 0) ;
<br>
<br>NoOfData = 8000 ; % Set no of data points used for training
<br>Order = 32 ; % Set the adaptive filter order
<br>
<br>Lambda = 0.98 ; % Set the forgetting factor
# N* a% q0 L( W) l3 \$ O" _<br>Delta = 0.001 ; % R initialized to Delta*I
<br>
+ K4 ^2 t5 e( A* n3 b- [3 k3 w<br>x = randn(NoOfData, 1) ;% Input assumed to be white
9 E8 y9 ^/ ?5 m. c3 H. g' e<br>h = rand(Order, 1) ; % System picked randomly
<br>d = filter(h, 1, x) ; % Generate output (desired signal)
- }, I$ K# c8 Q6 T1 j<br>
5 ^$ W! W- p0 i<br>% Initialize RLS
# P9 |2 r3 d' P- o& v8 J2 a0 N<br>
<br>P = Delta * eye ( Order, Order ) ;
<br>w = zeros ( Order, 1 ) ;
9 p3 f7 h* l6 T8 E3 A<br>
' y8 \, w9 y8 c4 P<br>% RLS Adaptation
<br>
/ e) N2 b2 x* j; z) j<br>for n = Order : NoOfData ;
/ l* D  V9 `* R" n  {  J: @<br>
# f; p6 r! n0 l- f& q<br>u = x(n:-1:n-Order+1) ;
* H0 r5 _( s6 f9 h: z) u<br>pi_ = u' * P ;
) C( ?. @, |- |$ s  E1 X' f- M# b<br>k = Lambda + pi_ * u ;
<br>K = pi_'/k;
<br>e(n) = d(n) - w' * u ;
" z2 N/ ?1 ]  O* b8 x<br>w = w + K * e(n) ;
<br>PPrime = K * pi_ ;
<br>P = ( P - PPrime ) / Lambda ;
+ h! c; t/ J; i! Y; W1 r- c# @/ ^1 x<br>w_err(n) = norm(h - w) ;
# b" R. w! E4 q4 r<br>
9 K  F' b- r; V) B( g<br>end ;
<br>
<br>% Plot results
% f6 F: J" g" t) w6 h- e<br>
3 V8 R1 K% k& f2 r* v& D( u. D<br>figure ;
6 c4 E( s# y( O7 e. `% g8 ?% g4 S<br>plot(20*log10(abs(e))) ;
" k) T# n& e. i: e: y+ y7 d1 N<br>title('Learning Curve') ;
<br>xlabel('Iteration Number') ;
<br>ylabel('Output Estimation Error in dB') ;
<br>
# a- r0 g: n) K/ b<br>figure ;
, a2 k. w+ a& G! \* u( h* q( `3 I<br>semilogy(w_err) ;
- Y2 c4 u2 L1 F" @<br>title('Weight Estimation Error') ;
<br>xlabel('Iteration Number') ;
<br>ylabel('Weight Error in dB') ;
. s% c" ?/ j( R  _) U<br>
5 @0 g/ S  o. K( |( B! L0 r  e

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