代码搜索：Sampling
找到约 3,969 项符合「Sampling」的源代码
代码结果 3,969
www.eeworm.com/read/220289/14843738
m demolgd1.m
%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by sampling X
www.eeworm.com/read/220289/14843858
m demrbf1.m
%DEMRBF1 Demonstrate simple regression using a radial basis function network.
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by sampling
www.eeworm.com/read/220289/14843906
m metrop.m
function [samples, energies, diagn] = metrop(f, x, options, gradf, varargin)
%METROP Markov Chain Monte Carlo sampling with Metropolis algorithm.
%
% Description
% SAMPLES = METROP(F, X, OPTIONS) use
www.eeworm.com/read/216045/15028685
m ip_07_01.m
% MATLAB script for Illustrated Problem 7.1.
clear
echo on
T=1;
delta_T=T/200; % sampling interval
alpha=0.5; % rolloff factor
fc=40/T; % carrier frequency
www.eeworm.com/read/216045/15028905
m ip_06_03.m
% MATLAB script for Illustrative Problem 6.3.
clear
echo on
f_cutoff=2000; % the desired cutoff frequency
f_stopband=2500; % the actual stopband frequency
fs=10000; % the sampling frequen
www.eeworm.com/read/215197/15070790
m lpcbwexp.m
function arx=lpcbwexp(ar,bw)
%LPCBWEXP expand formant bandwidths of LPC filter ARX=(AR,BW)
%minimum bandwidth will be BW*fs where fs is the sampling frequency
%the radius of each pole will be multi
www.eeworm.com/read/214970/15081477
m lpcbwexp.m
function arx=lpcbwexp(ar,bw)
%LPCBWEXP expand formant bandwidths of LPC filter ARX=(AR,BW)
%minimum bandwidth will be BW*fs where fs is the sampling frequency
%the radius of each pole will be multi
www.eeworm.com/read/214301/15107057
m extr.m
%EXTR finds extrema and zero-crossings
%
% [indmin, indmax, indzer] = EXTR(x,t)
%
% inputs : - x : analyzed signal
% - t (optional) : sampling times, default 1:length(x)
%
% outputs : - indm
www.eeworm.com/read/212307/15160045
m demolgd1.m
%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by sampling X
www.eeworm.com/read/212307/15160167
m demrbf1.m
%DEMRBF1 Demonstrate simple regression using a radial basis function network.
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by sampling