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edu^~jnoonan^ http:^^www.cs.tufts.edu^~jnoonan^

Date: Mon, 25 Nov 1996 22:50:08 GMT Server: Apache/1.0.5 Content-type: text/html Content-length: 2933 Last-modified: Wed, 24 Apr 1996 17:34:29 GMT Joe Noonan Joe Noon
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readme

-- ReBEL : Recursive Bayesian Estimation Library -- A Matlab toolkit for Recursive Bayesian Estimation Copyright 2002, Rudolph van der Merwe 1) WHAT IS ReBEL ? Re
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m rlsfilter.m

% 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 fac
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m chap7_10f.m

%Discrete Kalman filter %x=Ax+B(u+w(k)); %y=Cx+D+v(k) function [u]=kalman(u1,u2,u3) persistent A B C D Q R P x yv=u2; if u3==0 x=zeros(2,1); ts=0.001; a=25;b=133; sys=tf(b,[1,a
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html index.html

probab/estimation
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html contents.html

Contents.m
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m contents.m

% Probability distribution functions. % % estimation - (dir) Probability distribution estimation. % % dsamp - Generates samples from discrete distribution. % erfc2 - Normal cumulative dis
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m chap7_10f.m

%Discrete Kalman filter %x=Ax+B(u+w(k)); %y=Cx+D+v(k) function [u]=kalman(u1,u2,u3) persistent A B C D Q R P x yv=u2; if u3==0 x=zeros(2,1); ts=0.001; a=25;b=133; sys=tf(b,[1,a
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m chap7_10f.m

%Discrete Kalman filter %x=Ax+B(u+w(k)); %y=Cx+D+v(k) function [u]=kalman(u1,u2,u3) persistent A B C D Q R P x yv=u2; if u3==0 x=zeros(2,1); ts=0.001; a=25;b=133; sys=tf(b,[1,a
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html index.html

probab/estimation