代码搜索：NetWork
找到约 10,000 项符合「NetWork」的源代码
代码结果 10,000
www.eeworm.com/read/381141/9107760
loc powerresman.loc
/**
*
* Resource file containing English strings for PowerResMan application
*
* Copyright (c) 2004 Nokia Corporation
* version 2.0
*/
// APPLICATION INFORMATION
#define ELanguage ELangE
www.eeworm.com/read/381005/9116503
m demopsonet.m
% demoPSOnet.m
% script to show a quick, uncomplicated demo of using trainpso for training
% a neural net
%
% tries to build a feedforward neural net to approximate a noisy increaing
% sin funct
www.eeworm.com/read/281872/9128228
ifup-hdlc
#!/bin/sh
PATH=/sbin:/usr/sbin:/bin:/usr/bin
cd /etc/sysconfig/network-scripts
. network-functions
CONFIG=$1
source_config
if [ "foo$2" = "fooboot" -a "${ONBOOT}" = "no" ]
then
exit
fi
if [ -z "$
www.eeworm.com/read/380747/9129866
m elman_app.m
%Elman Application
%
clf
figure(gcf)
setfsize(500,500);
echo on
% MEWELM —— 建立一个Elman神经网络
% TRAIN —— 训练一个神经网络
% SIM —— 对一个神经网络进行仿真
pause %Strik any key to creat a network
clc
P1=sin(1:
www.eeworm.com/read/380477/9146067
cpp spikeinput.cpp
/***************************************************************************
spikeinput.cpp - description
-------------------
begin
www.eeworm.com/read/380453/9148171
readme
This directory contains the lwneuralnet library itself. Type 'make'
to compile the library.
To use the library, #include "lwneuralnet.h" in your C/C++ application
and link with liblwneuralnet.a. The
www.eeworm.com/read/183443/9158846
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/183443/9158976
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/183443/9158984
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a dag-svm multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%