代码搜索：ESTIMATION
找到约 3,786 项符合「ESTIMATION」的源代码
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www.eeworm.com/read/312163/13617592
m contents.m
% Probability distribution functions.
%
% estimation - (dir) Probability distribution estimation.
%
% dsamp - Generates samples from discrete distribution.
% erfc2 - Normal cumulative dis
www.eeworm.com/read/303058/13822631
m kf_cwpa_demo.m
% Demonstration for Kalman filter and smoother using a 2D CWPA model
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or lat
www.eeworm.com/read/301446/13859140
m rls.m
function RLS()
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 f
www.eeworm.com/read/147682/5728111
m plot_anvc.m
% plot_anvc(w,p,s,e,a,b)
%
% Generates plots for evaluating an adaptive active
% noise and vibration control problem.
%
% Input variables [Size]:
% w : estimated impulse response [L x
www.eeworm.com/read/213769/6282519
m ofdmlsechannelestimation.m
% OFDM LSE Channel Estimation
%
% --------------------------------------------------------------------------------
%
% Author: Hamid Ramezani
% Summary: the performance of LSE channel estimat
www.eeworm.com/read/266962/6290232
m example_dd2.m
%% Example of the DD2 filter implementation usage
clear all
close all
useMatlabSymbolicToolbox = false;
%% Definition of the pdf's within the NFT framework
%
disp('*********************************
www.eeworm.com/read/266962/6290233
m example_ukf.m
%% Example of the UKF filter implementation usage
clear all
close all
useMatlabSymbolicToolbox = false;
%% Definition of the pdf's within the NFT framework
%
disp('*********************************
www.eeworm.com/read/266962/6290244
m example_local_filters.m
%% Example of the sigma point local filters and of the Extended Kalman Filter implementation usage
clear all
close all
useMatlabSymbolicToolbox = false;
%% Definition of the pdf's within the NFT fr
www.eeworm.com/read/493294/6400240
m parzenml.m
%PARZENML Optimum smoothing parameter in Parzen density estimation.
%
% H = PARZENML(A,FID)
%
% INPUT
% A input dataset
% FID File ID to write progress to (default [], see PRPROGRESS)
%
%