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/303438/13816160
m sgrpdlay.m
function [gd,fnorm]=sgrpdlay(x,fnorm);
%SGRPDLAY Group delay estimation of a signal.
% [GD,FNORM]=SGRPDLAY(X,FNORM) estimates the group delay of
% signal X at the normalized frequency(ies) FNORM.
%
%
www.eeworm.com/read/291067/6302827
m eetimepreloop.m
function eeTVOut = eetimepreloop(eeTVIn, noTrials, noLoop1, noLoop2, noLoop3)
%EETIMEPRELOOP Pre-loop preparation of estimation of execution time.
%
%--------
%Synopsis:
% eeTVOut = eetimepreloop(ee
www.eeworm.com/read/463288/6305941
m ch4_1h.m
% Select a demo number: 9
% In this demo we consider spectrum estimation, using Marple's
% test case (The complex data in L. Marple: S.L. Marple, Jr,
% Digital Spectral Analysi
www.eeworm.com/read/331502/6327369
m bispecd.m
function [Bspec,waxis] = bispecd (y, nfft, wind, nsamp, overlap)
%BISPECD Bispectrum estimation using the direct (fft-based) approach.
% [Bspec,waxis] = bispecd (y, nfft, wind, segsamp, overlap)
www.eeworm.com/read/233611/6336696
m ofdmce.m
% ofdmce.m
%
% Simulation program to realize OFDM transmission system
%
% GI CE GI data GI data...(data 6symbols)
% (CE: Chanel estimation symbol, GI Guard interval)
%
%**********************
www.eeworm.com/read/359185/6352497
m minimum_cost.m
function D = Minimum_Cost(train_features, train_targets, lambda, region)
% Classify using the minimum error criterion via histogram estimation of the densities
% Inputs:
% features- Train featur
www.eeworm.com/read/493206/6398475
m minimum_cost.m
function D = Minimum_Cost(train_features, train_targets, lambda, region)
% Classify using the minimum error criterion via histogram estimation of the densities
% Inputs:
% features- Train featur
www.eeworm.com/read/487211/6516779
asv searchmaxmin.asv
close all;
clear all;
%normalized missalarm
%to produce the missing alarm of SIR state estimation and smoothed residual
n = 1:600;%sample steps
stdw = sqrt(10);
ngrid = 50;
npar = 500;%particl
www.eeworm.com/read/487211/6516783
m searchmaxmin.m
close all;
clear all;
%normalized missalarm
%to produce the missing alarm of SIR state estimation and smoothed residual
n = 1:600;%sample steps
stdw = sqrt(10);
ngrid = 50;
npar = 500;%particl
www.eeworm.com/read/485544/6552627
m demse2.m
% DEMSE2 Demonstrate state estimation on a simple scalar nonlinear (time variant) problem
%
% See also
% GSSM_N1
% Copyright (c) Rudolph van der Merwe (2002)
%
% This file is part of