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/339665/12211689
m rbfpak.m
function w = rbfpak(net)
%RBFPAK Combines all the parameters in an RBF network into one weights vector.
%
% Description
% W = RBFPAK(NET) takes a network data structure NET and combines the
% componen
www.eeworm.com/read/339665/12211937
m netevfwd.m
function [y, extra, invhess] = netevfwd(w, net, x, t, x_test, invhess)
%NETEVFWD Generic forward propagation with evidence for network
%
% Description
% [Y, EXTRA] = NETEVFWD(W, NET, X, T, X_TEST) tak
www.eeworm.com/read/253585/12213077
m char3.m
%% Character Recognition Example (III):Training a Simple NN for
%% classification
%% Read the image
I = imread('sample.bmp');
%% Image Preprocessing
img = edu_imgpreprocess(I);
for cnt = 1:5
www.eeworm.com/read/253518/12217774
scp netbufeventset.scp
# NetBufEventSet.scp - WindView event points for network buffer management.
#
# modification history
# --------------------
# 01a,12dec97,spm created.
#
# DESCRIPTION
# Add basic instrumentation for
www.eeworm.com/read/339450/12235478
kconfig
#
# Acorn Network device configuration
# These are for Acorn's Expansion card network interfaces
#
config ARM_AM79C961A
bool "ARM EBSA110 AM79C961A support"
depends on NET_ETHERNET && ARM && ARCH_E
www.eeworm.com/read/150905/12248251
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/150905/12248413
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/150905/12249872
m mdnpak.m
function w = mdnpak(net)
%MDNPAK Combines weights and biases into one weights vector.
%
% Description
% W = MDNPAK(NET) takes a mixture density network data structure NET
% and combines the network w
www.eeworm.com/read/150905/12249878
m demolgd1.m
%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by sampling X