代码搜索:NetWork
找到约 10,000 项符合「NetWork」的源代码
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www.eeworm.com/read/386942/8717043
plg neighborinfo.plg
Build Log
--------------------Configuration: NeighborInfo - Win32 Debug--------------------
Command Lines
Creating command line "rc.exe /l 0x80
www.eeworm.com/read/386806/8726360
makefile
#
# Makefile for the linux networking.
#
# Note! Dependencies are done automagically by 'make dep', which also
# removes any old dependencies. DON'T put your own dependencies here
# unless it's s
www.eeworm.com/read/286732/8747159
cpp listview.cpp
#include "listview.h"
#include
#include
#include
#include
ListDemo::ListDemo( QWidget *parent, const char *name )
: QWidget( parent, name )
{
www.eeworm.com/read/286526/8761879
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/430197/8762169
pas unitnetwork.pas
unit UnitNetwork;
interface
uses
Windows,
Messages,
SysUtils,
Variants,
Classes,
Graphics,
Controls,
Forms,
Dialogs,
ExtCtrls,
Menus,
ComCtrls,
StdCtrls,
www.eeworm.com/read/386050/8767263
m ffnc.m
%FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
%
www.eeworm.com/read/386050/8767500
m bpxnc.m
%BPXNC Back-propagation trained feed-forward neural net classifier
%
% [W,HIST] = BPXNC (A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% A Dataset
% UNITS Array indicating number of units in each h
www.eeworm.com/read/429878/8783797
htm mdnpak.htm
Netlab Reference Manual mdnpak
mdnpak
Purpose
Combines weights and biases into one weights vector.
Synopsis
w =
www.eeworm.com/read/429878/8783825
htm rbfjacob.htm
Netlab Reference Manual rbfjacob
rbfjacob
Purpose
Evaluate derivatives of RBF network outputs with respect to inputs.
Synop
www.eeworm.com/read/429878/8783838
htm mlpfwd.htm
Netlab Reference Manual mlpfwd
mlpfwd
Purpose
Forward propagation through 2-layer network.
Synopsis
y = mlpfwd(