qpyujco60.rar_LMS学习曲线_扩散LMS_特征值扩散度_自适应均衡

% lms_eq.m - use multidimensional LMS algorithm to estimate channel response % written for MATLAB 4.0 % % Input parameters: % Xi : matrix of training/test points - each row is % considered a datum % y : desired vector output values corresponding to Xi % verbose: set to 1 for interactive processing % alpha : the initial value of step size % decay : set to 1 for O(1/n) decay in alpha Nin = size(Xi, 2); Nout = size(Y, 2); % maximum number of timesteps that can be predicted N = size(Xi, 1); % initialize weight matrix and associated parameters for LMS predictor W = zeros(Nout, Nin); Wo = []; for n = 1:N, % save weights Wo = [Wo W']; % predict next sample and error xp(n, :) = Xi(n, :) * W'; e(n, :) = Y(n, :) - xp(n, :); ne(n) = norm(e(n, :)); if (rp.verbose ~= 0) disp(['time step ', int2str(n), ': mag. pred. err. = ' , num2str(ne(n)) ] ); end; % adapt weight matrix and step size %W = W + rp.mu * e(n, :)' * Xi(n, :); W = W + (rp.mu./(1+Xi(n, :)'*Xi(n, :)) * e(n, :)' * Xi(n, :); if (rp.decay == 1) rp.mu = rp.mu * n/(n+1); % use O(1/n) decay rate end; end % for n