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/400577/11573210
m parzenmls.m
%PARZENML Optimum smoothing parameter in Parzen density estimation.
% Soft label version
%
% H = PARZENML(A,FID)
%
% INPUT
% A input dataset
% FID File ID to write progress to (def
www.eeworm.com/read/342008/12047242
m parzenml.m
%PARZENML Optimum smoothing parameter in Parzen density estimation.
%
% h = parzenml(A)
%
% Maximum likelihood estimation for the smoothing parameter in the
% Parzen denstity estimation of the dat
www.eeworm.com/read/254742/12121189
m program_10_1.m
% Program 10_1
% Estimation of FIR Filter Order Using remezord
%
fedge = input('Type in the bandedges = ');
mval = input('Desired magnitude values in each band = ');
dev = input('Allowable deviat
www.eeworm.com/read/151781/12176066
txt readme.txt
lencodJM20Original.exe ---- full integer search
lencodJM20Dyna.exe ---- integer search with dynamic range decision
lencodJM20Fast.exe ---- integer search with fast algorithm proposed by t
www.eeworm.com/read/253868/12179684
txt cauchy.txt
Cauchy cdf, pdf, inverse cdf, parameter fit, and random generator.
Implementation package of the Cauchy distribution.
cauchycdf: Cauchy cumulative distribution function (cdf).
cauchyfit: Parameter
www.eeworm.com/read/148789/12425868
rd sigest.rd
\name{sigest}
\alias{sigest}
\alias{sigest,formula-method}
\alias{sigest,matrix-method}
\title{Hyperparameter estimation for the Gaussian Radial Basis kernel}
\description{
Given a range of values f
www.eeworm.com/read/231449/14233629
m program_07_5.m
% Program 7_5
% Estimation of FIR Filter Order Using remezord
%
fedge = input('Type in the bandedges = ');
mval = input('Desired magnitude values in each band = ');
dev = input('Allowable deviati
www.eeworm.com/read/124397/14569783
m lpcsyn.m
function xhat = lpcsyn(A,P,G,m)
% lpcsyn --> Synthesized speech from LP parameters.
%
%
% xhat = lpcsyn(A,P,G,m)
%
%
% The function takes the AR parameters A, the pit
www.eeworm.com/read/220289/14843703
m dempe1.m
% DEMPE1 Demonstrate parameter estimation on a simple 2nd order LTI system.
%
% This is a simple demonstration of how to use the ReBEL toolkit for parameter estimation on
% a simple 2nd order
www.eeworm.com/read/220289/14843705
m demje1.m
% DEMJE1 Demonstrate joint estimation on a 2nd order LTI system.
%
% This is a demonstration of how to use the ReBEL toolkit for joint estimation on
% a simple 2nd order LTI system.
%
% Se