代码搜索：corresponding
找到约 4,250 项符合「corresponding」的源代码
代码结果 4,250
www.eeworm.com/read/173140/9671045
m analytic.m
function z=analytic(x)
% z=analytic(x)
%ANALYTIC Returns the analytic signal corresponding to signal x.
%
z=hilbert(x);
www.eeworm.com/read/172476/9705778
m gregorian.m
function [gtime]=gregorian(julian)
% GREGORIAN Converts Julian day numbers to corresponding
% Gregorian calendar dates. Although formally,
% Julian days start and end at noon, here Juli
www.eeworm.com/read/171239/9765089
m analytic.m
function z=analytic(x)
% z=analytic(x)
%ANALYTIC Returns the analytic signal corresponding to signal x.
%
z=hilbert(x);
www.eeworm.com/read/170936/9779169
m netinit.m
function net = netinit(net, prior)
%NETINIT Initialise the weights in a network.
%
% Description
%
% NET = NETINIT(NET, PRIOR) takes a network data structure NET and sets
% the weights and biases by s
www.eeworm.com/read/170936/9779240
m netunpak.m
function net = netunpak(net, w)
%NETUNPAK Separates weights vector into weight and bias matrices.
%
% Description
% NET = NETUNPAK(NET, W) takes an net network data structure NET and a
% weight vect
www.eeworm.com/read/170936/9779389
m mlpprior.m
function prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)
%MLPPRIOR Create Gaussian prior for mlp.
%
% Description
% PRIOR = MLPPRIOR(NIN, NHIDDEN, NOUT, AW1, AB1, AW2, AB2) generates a
% dat
www.eeworm.com/read/415313/11076395
m netinit.m
function net = netinit(net, prior)
%NETINIT Initialise the weights in a network.
%
% Description
%
% NET = NETINIT(NET, PRIOR) takes a network data structure NET and sets
% the weights and biases by s
www.eeworm.com/read/415313/11076496
m netunpak.m
function net = netunpak(net, w)
%NETUNPAK Separates weights vector into weight and bias matrices.
%
% Description
% NET = NETUNPAK(NET, W) takes an net network data structure NET and a
% weight vect
www.eeworm.com/read/415313/11076707
m mlpprior.m
function prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)
%MLPPRIOR Create Gaussian prior for mlp.
%
% Description
% PRIOR = MLPPRIOR(NIN, NHIDDEN, NOUT, AW1, AB1, AW2, AB2) generates a
% dat
www.eeworm.com/read/413912/11137111
m netinit.m
function net = netinit(net, prior)
%NETINIT Initialise the weights in a network.
%
% Description
%
% NET = NETINIT(NET, PRIOR) takes a network data structure NET and sets
% the weights and biases by s