代码搜索：recurrent
找到约 100 项符合「recurrent」的源代码
代码结果 100
www.eeworm.com/read/291420/8421126
m recurrent.m
%extremum_RNN
clear;
clc;
simustep=0.001;
theta=4;
x1=4;
x2=0;
c=[0 1 0]';
y=[20 20 0.01]';
z2=[0 0 0]';
mu=0.25;%mu=5.6555;
t=0;
j=1;
for i=0:150000
A2=[1 -sign(y(1)) 0;-20*(x1-5)*x
www.eeworm.com/read/163251/10168510
cpp recurrent.cpp
/*
nn-utility (Provides neural networking utilities for c++ programmers)
Copyright (C) 2003 Panayiotis Thomakos
This library is free software; you can redistribute it and/or
modi
www.eeworm.com/read/163251/10168550
dsp recurrent.dsp
# Microsoft Developer Studio Project File - Name="recurrent" - Package Owner=
# Microsoft Developer Studio Generated Build File, Format Version 6.00
# ** DO NOT EDIT **
# TARGTYPE "Win32 (x86)
www.eeworm.com/read/163251/10168570
help recurrent.help
"recurrent.cpp" Help File
Purpose: Test if a recurrent network
can learn how to copy input. This
really just tests the functionality of
recurrent neural nets.
~Panayiotis Thomakos
www.eeworm.com/read/349415/10828256
cpp recurrent.cpp
/*
nn-utility (Provides neural networking utilities for c++ programmers)
Copyright (C) 2003 Panayiotis Thomakos
This library is free software; you can redistribute it and/or
modi
www.eeworm.com/read/349415/10828306
dsp recurrent.dsp
# Microsoft Developer Studio Project File - Name="recurrent" - Package Owner=
# Microsoft Developer Studio Generated Build File, Format Version 6.00
# ** DO NOT EDIT **
# TARGTYPE "Win32 (x86)
www.eeworm.com/read/349415/10828319
help recurrent.help
"recurrent.cpp" Help File
Purpose: Test if a recurrent network
can learn how to copy input. This
really just tests the functionality of
recurrent neural nets.
~Panayiotis Thomakos
www.eeworm.com/read/191902/8417372
m backpropagation_recurrent.m
function [D, Wh, Wo] = Backpropagation_Recurrent(train_features, train_targets, params, region)
% Classify using a backpropagation recurrent network with a batch learning algorithm
% Inputs:
% f
www.eeworm.com/read/286662/8751969
m backpropagation_recurrent.m
function [test_targets, W, J] = Backpropagation_Recurrent(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation recurrent network with a batch learning algorithm
www.eeworm.com/read/177129/9468995
m backpropagation_recurrent.m
function [D, Wh, Wo] = Backpropagation_Recurrent(train_features, train_targets, params, region)
% Classify using a backpropagation recurrent network with a batch learning algorithm
% Inputs:
% f