... particularly sure what final outcome is being sought. Neuralnetworks are often
employed in data mining do to the ability for neuralnetworks to be trained.
Neural networks can also be used ... Understanding NeuralNetworks
Article Title:
Chapter 2: Understanding Neural Networks
Category: Artificial Intelligence Most Popular
From Series:
Programming NeuralNetworksinJava
Posted: ... operator. Yet
neural networks have a long way to go.
Neural Networks Today
Neural networks are in use today for a wide variety of tasks. Most people think of neural
networks attempting to emulate...
... combined to create the training data for the XOR operator. The following line of code
combines these two arrays to create training data:
NeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, ... » Neural Networks
An Introduction to Encog NeuralNetworks for Java
By JeffHeaton, 17 Jan 2010
Download source code - 306 KB
Introduction
This article provides a basic introduction to neuralnetworks ... be trained before they are of any use. To train this neural network, me must provide training
data. The training data is the truth table for the XOR operator. The XOR has the following inputs:
public...
... take
considerably more training iterations.
We begin by creating a training set.
TrainingSet trainingSet = new TrainingSet(2, 1);
trainingSet.addElement(new SupervisedTrainingElement
(new double[]{0, ... DynamicBackPropagation();
train.setNeuralNetwork(network);
network.setLearningRule(train);
We now begin to loop through training iterations, until we are trained to below 1%.
int epoch = 1;
do
{
train.doOneLearningIteration(trainingSet);
... the trained network’s results.
System.out.println(" ;Neural Network Results:");
for(TrainingElement element : trainingSet.trainingElements()) {
network.setInput(element.getInput());
...
... the International Joint Conference
on NeuralNetworks (IJCNN) meetings in Washington, DC, in 2001, and
in Honolulu and Singapore in 2002. These meetings were eye-openers for
anyone trained in ... polynomal.
Neural Networksin Finance:
Gaining Predictive Edge
in the Market
8 1. Introduction
The financial sectors of emerging markets, in particular, but also in
markets with a great deal of innovation ... forms in the neural network literature.
2.4.2 Squasher Functions
The neurons process the input data in two ways: first by forming lin-
ear combinations of the input data and then by “squashing”...
... Joint Conference
on NeuralNetworks (IJCNN) meetings in Washington, DC, in 2001, and
in Honolulu and Singapore in 2002. These meetings were eye-openers for
anyone trained in classical statistics ... by selecting “Customer Support” and then “Obtaining Permissions.”
Library of Congress Cataloging -in- Publication Data
McNelis, Paul D.
Neural networksin finance : gaining predictive edge in the ... Nonlinear Principal Components: Intrinsic
Dimensionality 41
2.6.1 Linear Principal Components 42
2.6.2 Nonlinear Principal Components 44
2.6.3 Application to Asset Pricing 46
2.7 Neural Networks...
... training algorithms.
Supervised training is not the only training option. Chapter 9,
“Unsupervised Training Methods” shows how to use unsupervised training
with Encog. Unsupervised training ...
Training
Training Set
XOR Operator
48
Programming NeuralNetworks with Encog 2 inJava
Some NeuralLogic classes require specific layer types. For the
NeuralLogic classes to find ... inJava
vi
Programming NeuralNetworks with Encog 2 inJava
Publisher: Heaton Research, Inc
Programming NeuralNetworks with Encog 2 inJava
March, 2010
Author: Jeff Heaton...
... for use in
training and testing the neural network. A large training data reduces
the risk of under-sampling the nonlinear function, but increases the
training time. To improve training, preprocessing ...
minmax
min
VV
VV
A
−
−
=
(4)
Training was performed iteratively until the average of sum squared
error over all the training patterns was minimized. Experiment were
carried out using ... as shown in Fig.1. The implementation of the back-
propagation neural network model for predicting proper strain rate
involved three phases
First, data collection phase involved gathering the...
... before or during machining.
The primary objective was to train the fuzzy system by generating fuzzy rules from input–output pairs,
and combining these generated and linguistic rules into a common ... inspecting machined
surfaces at fixed intervals. A surface profilometer containing a contact stylus is used in the manual
inspection procedure. This procedure is both time-consuming and labor-intensive. ... laboratories in the state of Iowa (including Winnebago Co. in Forest City;
Delavan Inc. in Des Moines; Sauer-Sundstrand Inc. in Ames), point to the feasibility of in- process surface
roughness recognition...
... (http://trantor.bioc.columbia.edu/cgi-bin/SPIN/),
which contains all the protein complexes contained in the
PDB Protein Data Bank. Using the
SPIN
search engine, it is
possible to search the se t of protein complexes for ... s ame as including the residue
conservation in the contact surface in the protein family.
The scoring efficiency of the best performing neural
network in t he testing phase is shown in Table 1. ... protein–protein interaction; protein surface;
neural network; evolutionary information.
In the Ôpost-genomeÕ era, a shift of emphasis is taking place
towards making genomics functional [1,2]. In...
... furniture
articles
◦
Spray finishing & sand sealing
◦ Marketing & Pricing
◦ Lifting; loading & offloading
◦ Sanding
◦
Packing
◦ Record
keeping
◦
Cleaning
◦ Customer
service ... Lifting; loading and offloading; sawmilling; carpentry;
spray finishing; physical sourcing of raw materials like during log auctions; relief carving;
saw doctoring and repairing of other machines. ... may include the unpaid family
members. Furniture Warehouses in Jepara arguably deal mostly in unfinished
furniture articles thereby engaging in finishing activities such as sanding; varnishing;...
...
should be divided into several sets (training, testing, production, on-line, remaining). The
training set is used to adjust the interconnection weights of the MPNN model. The testing
set is used ... feedforward networks. Neural
Networks 4, pp. 251-257
Kohonen, T. (1995). Self-organizing maps. Springer, Berlin
Kurkova, V. (1992). Kolmogorov’s Theorem and Multilayer Neural Networks, Neural
Networks, ... stuck in a local minimum far from the global one. During the learning process,
the network should be periodically tested on the testing set (not included in the training set)
www.intechopen.com
...