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/393518/8280858
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/393211/8304408
asv rwg6.asv
%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/393211/8304478
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/393211/8304718
asv rwg6.asv
%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/393211/8304746
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/170936/9779152
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/170936/9779342
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/170936/9779394
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/415313/11076371
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/415313/11076641
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