代码搜索：NetWork
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www.eeworm.com/read/192357/8384947
howto
Hello everybody,
Although there is a man page which documents most of the actual
commands, there is still a 'gap' concerning what bridges are, and how
to set them up. This document attempts to fill t
www.eeworm.com/read/291799/8394683
m artmapexample.m
% ARTMAPExample.m
% Aaron Garrett
%
% This script uses the ARTMAP network to learn the exclusive-or (XOR) function.
% Set up the input and supervisory signal for the XOR function.
input = [1,
www.eeworm.com/read/190387/8444222
m hop_test.m
function [s, count, M]=hop_test(W,x,update)
% function [s, count, M]=hop_test(W,x,update)
%
% s - output state vector
% count - number of cyckes until stable state is reached
% M - matrix conta
www.eeworm.com/read/190387/8444286
m hop_stor.m
function W=hop_stor(P)
% function W=hop_stor(P)
%
% performs the storage (learning phase) for a Hopfield network
%
% W - weight matrix
% P - patterns to be stored (column wise matrix)
%
% Hugh
www.eeworm.com/read/290372/8487387
c sysend.c
/* sysEnd.c - System Enhanced Network interface support library */
/* Copyright 1984-2001 Wind River Systems, Inc. */
#include "copyright_wrs.h"
/*
modification history
--------------------
01a,12ap
www.eeworm.com/read/389844/8496301
m elm_rtrain.m
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% ELM_Rtrain(network,data) - train with Resursive Least Squares
%
% Parameters: network - neural network with mat
www.eeworm.com/read/389844/8496308
m nn_sim.m
function f=NN_sim(network,data)
Y = data.target';
for i=1:length(Y)
s = 0;
for j=1:network.regressors
s = s + network.weights(j)*tansig( network.w(j,:) * data.vtraining(:,i)+
www.eeworm.com/read/389844/8496324
m elm_train.m
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% ELM_train(network,data) - train MNN model with ELM (standard mode) .
%
% Parameters: network - neural network w
www.eeworm.com/read/389844/8496332
m velm_train.m
function f=velm_train(network,data)
Y = data.target';
% calculate hidden layer output matrix
for i=1:length(Y)
for j=1:network.regressors
X(i,j) = tanh( network.w(j,:) * data.vtraining
www.eeworm.com/read/389592/8511059
c sysend.c
/* sysEnd.c - System Enhanced Network interface support library */
/* Copyright 1984-2001 Wind River Systems, Inc. */
#include "copyright_wrs.h"
/*
modification history
--------------------
01a,12ap