代码搜索：Gradient
找到约 2,951 项符合「Gradient」的源代码
代码结果 2,951
www.eeworm.com/read/325790/13184798
m gradw.m
function v=gradw(obj,x,t,w,u)
% Evaluates the gradient object at time t, with respect to w.
%
% Syntax: (* = optional)
%
% v = gradw(obj, x, t, w, u);
%
% In arguments:
%
% 1. obj
% The xltv
www.eeworm.com/read/325790/13184811
m gradx.m
function v=gradx(obj,x,t,w,u)
% Evaluates the gradient object at time t, with respect to x.
%
% Syntax: (* = optional)
%
% v = gradx(obj, x, t, w, u);
%
% In arguments:
%
% 1. obj
% The xtab
www.eeworm.com/read/325790/13184864
m gradw.m
function v=gradw(obj,x,t,w,u)
% Evaluates the gradient object at time t, with respect to w.
%
% Syntax: (* = optional)
%
% v = gradw(obj, x, t, w, u);
%
% In arguments:
%
% 1. obj
% The xtab
www.eeworm.com/read/305884/13758568
css forum_admin.css
body {
filter : progid:DXImageTransform.Microsoft.gradient(GradientType:1 ,startColorStr=#10234B,endColorStr=#183789);
font: tahoma,verdana,arial,helvetica,sans-serif;
background:#10234B; font-si
www.eeworm.com/read/130196/5963038
m optimize.m
function [co, p, fret, its]=optimize(co, p, param)
% [co, p, fret, iter]=cg_optimizable.optimize(co, p)
%
% The Fletcher-Reeves-Polak-Ribiere-Conjugate Gradient Algorithm
% (from Numerical Recipe
www.eeworm.com/read/120147/6303510
m nnd12cg.m
function nnd12cg(cmd,arg1)
%NND12CG Conjugate gradient backpropagation demonstration.
%
% This demonstration requires the Neural Network Toolbox.
% First Version, 8-31-95.
%==================
www.eeworm.com/read/263805/11341576
m nnd12cg.m
function nnd12cg(cmd,arg1)
%NND12CG Conjugate gradient backpropagation demonstration.
%
% This demonstration requires the Neural Network Toolbox.
% First Version, 8-31-95.
%==================
www.eeworm.com/read/406594/11439422
m nnd12cg.m
function nnd12cg(cmd,arg1)
%NND12CG Conjugate gradient backpropagation demonstration.
%
% This demonstration requires the Neural Network Toolbox.
% First Version, 8-31-95.
%==================
www.eeworm.com/read/156528/11794879
m based_nag.m
function [y,PI] = based_nag()
%
%A natural gradient-based Algorithm for Blind Source Separation
%copyright 2005.4.14
%author:lucky zhang
%used to separate audio signal
%usage:[y,PI]=based_neg(x
www.eeworm.com/read/156528/11794926
m new_nag.m
function [y,PI] = new_nag()
%
%A natural gradient-based Algorithm for Blind Source Separation
%copyright 2005.4.14
%author:lucky zhang
%used to separate audio signal
%usage:[y,PI]=based_neg(x)