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/287843/8665654
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/381238/9100999
m rwg6.m
%RWG6 Plots the surface current distribution along the dipole
% Increase the number of sampling points, K, to obtain more
% accurate results
%
% Copyright 2002 AEMM. Revision 2002/03/11
www.eeworm.com/read/281807/9133229
m rwg6.m
%RWG6 Plots the surface current distribution along the dipole
% Increase the number of sampling points, K, to obtain more
% accurate results
%
% Copyright 2002 AEMM. Revision 2002/03/11
www.eeworm.com/read/380178/9158600
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/177674/9442393
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/177674/9442614
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/177674/9442680
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/176823/9483098
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/176823/9483301
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/176823/9483370
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