代码搜索：ESTIMATION
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www.eeworm.com/read/442564/6327574
m cp0803_rakeselector.m
% FUNCTION 8.11 : "cp0803_rakeselector"
%
% Simulates channel estimation for a discrete-time channel
% impulse response 'hf' with time resolution 'ts' in
% seconds. In addition, the function eva
www.eeworm.com/read/491223/6437606
readme
This package contains the Matlab source code of paper Q. Zhang, A. Zhou, Y. Jin. 'Modelling the Regularity in an Estimation of Distribution Algorithm for Continuous Multiobjective Optimisation with Va
www.eeworm.com/read/490882/6445099
m cp0803_rakeselector.m
% FUNCTION 8.11 : "cp0803_rakeselector"
%
% Simulates channel estimation for a discrete-time channel
% impulse response 'hf' with time resolution 'ts' in
% seconds. In addition, the function eva
www.eeworm.com/read/482374/6623681
src cml.src
/*
** cml.src CML - Maximum Likelihood Estimation with General
** Nonlinear Constraints on Parameters
**
** (C) Copyright 1994-1996 Aptech Systems, Inc.
** All Rights Reserved.
www.eeworm.com/read/479763/6678362
m matul.m
function [hest] = matul (bisp)
%MATUL Impulse response estimation using Matsuoka-Ulrych algorithm
% hest = matul(bisp)
% bisp - the estimated bispectrum
% (e.g., as computed by bispecd or
www.eeworm.com/read/478401/6716197
m demo2_02.m
%
% Demonstrates relative performance of Wiener filter (fixed-gain)
% and Kalman filter (time-varying gain) on random walk estimation
%
% Applied to random walk process with gaussian sampling nois
www.eeworm.com/read/478401/6716200
m rtsvskf.m
%
% Demonstrates relative performance of Kalman filter
% and Rauch-Tung-Striebel smoother on random walk estimation
%
clear all;
close all;
N = 100; % Number of samples of process used in simul
www.eeworm.com/read/408292/11398156
m fit_model_hmm.m
%main file for estimation prob graphical models
%plain hidden markov model
%see the examples in the example map for other specific models
%or define your own model
%to be done for each model
www.eeworm.com/read/158149/11641759
m matul.m
function [hest] = matul (bisp)
%MATUL Impulse response estimation using Matsuoka-Ulrych algorithm
% hest = matul(bisp)
% bisp - the estimated bispectrum
% (e.g., as computed by bispecd or
www.eeworm.com/read/155919/11838451
m cp0803_rakeselector.m
% FUNCTION 8.11 : "cp0803_rakeselector"
%
% Simulates channel estimation for a discrete-time channel
% impulse response 'hf' with time resolution 'ts' in
% seconds. In addition, the function eva