Programming Neural Networks in Java
Programming Neural Networks in Java will show the intermediate to advanced Java
programmer how to create neural networks. This book attempts to teach neural network
programming through two mechanisms. First the reader is shown how to create a reusable
neural network package that could be used in any Java program. Second, this reusable
neural network package is applied to several real world problems that are commonly faced
by IS programmers. This book covers such topics as Kohonen neural networks, multi layer
neural networks, training, back propagation, and many other topics.
Chapter 1: Introduction to Neural Networks
(Wednesday, November 16, 2005)
Computers can perform many operations considerably faster than a human being. Yet
there are many tasks where the computer falls considerably short of its human
counterpart. There are numerous examples of this. Given two pictures a preschool child
could easily tell the difference between a cat and a dog. Yet this same simple problem
would confound today's computers.
Chapter 2: Understanding Neural Networks
(Wednesday, November 16, 2005)
The neural network has long been the mainstay of Artificial Intelligence (AI) programming.
As programmers we can create programs that do fairly amazing things. Programs can
automate repetitive tasks such as balancing checkbooks or calculating the value of an
investment portfolio. While a program could easily maintain a large collection of images, it
could not tell us what any of those images are of. Programs are inherently unintelligent
and uncreative. Ordinary computer programs are only able to perform repetitive tasks.
Chapter 3: Using Multilayer Neural Networks
(Wednesday, November 16, 2005)
In this chapter you will see how to use the feed-forward multilayer neural network. This
neural network architecture has become the mainstay of modern neural network
programming. In this chapter you will be shown two ways that you can implement such a
neural network.
Chapter 4: How a Machine Learns
(Wednesday, November 16, 2005)
In the preceding chapters we have seen that a neural network can be taught to recognize
patterns by adjusting the weights of the neuron connections. Using the provided neural
network class we were able to teach a neural network to learn the XOR problem. We only
touched briefly on how the neural network was able to learn the XOR problem. In this
chapter we will begin to see how a neural network learns.
Chapter 5: Understanding Back Propagation
(Wednesday, November 16, 2005)
In this chapter we shall examine one of the most common neural network architectures--
the feed foreword back propagation neural network. This neural network architecture is
very popular because it can be applied to many different tasks. To understand this neural
network architecture we must examine how it is trained and how it processes the pattern.
The name "feed forward back propagation neural network" gives some clue as to both how
this network is trained and how it processes the pattern.
Chapter 6: Understanding the Kohonen Neural Network
(Wednesday, November 16, 2005)
In the previous chapter you learned about the feed forward back propagation neural
network. While feed forward neural networks are very common, they are not the only
architecture for neural networks. In this chapter we will examine another very common
architecture for neural networks.
Chapter 7: OCR with the Kohonen Neural Network
(Wednesday, November 16, 2005)
In the previous chapter you learned how to construct a Kohonen neural network. You
learned that a Kohonen neural network can be used to classify samples into several
groups. In this chapter we will closely examine a specific application of the Kohonen neural
network. The Kohonen neural network will be applied to Optical Character Recognition
(OCR).
Chapter 8: Understanding Genetic Algorithms
(Wednesday, November 16, 2005)
In the previous chapter you saw a practical application of the Kohonen neural network. Up
to this point the book has focused primarily on neural networks. In this and Chapter 9 we
will focus on two artificial intelligence technologies not directly related to neural networks.
We will begin with the genetic algorithm. In the next chapter you will learn about
simulated annealing. Finally Chapter 10 will apply both of these concepts to neural
networks. Please note that at this time JOONE, which was covered in previous chapters,
has no support for GAs’ or simulated annealing so we will build it.
Chapter 9: Understanding Simulated Annealing
(Wednesday, November 16, 2005)
In this chapter we will examine another technique that allows you to train neural networks.
In Chapter 8 you were introduced to using genetic algorithms to train a neural network.
This chapter will show you how you can use another popular algorithm, which is named
simulated annealing. Simulated annealing has become a popular method of neural network
training. As you will see in this chapter, it can be applied to other uses as well.
Chapter 10: Eluding Local Minima
(Wednesday, November 16, 2005)
In Chapter 5 backpropagation was introduced. Backpropagation is a very effective means
of training a neural network. However, there are some inherent flaws in the back
propagation training algorithm. One of the most fundamental flaws is the tendency for the
backpropagation training algorithm to fall into a “local minima”. A local minimum is a false
optimal weight matrix that prevents the backpropagation training algorithm from seeing
the true solution.
Chapter 11: Pruning Neural Networks
(Wednesday, November 16, 2005)
In chapter 10 we saw that you could use simulated annealing and genetic algorithms to
better train a neural network. These two techniques employ various algorithms to better fit
the weights of the neural network to the problem that the neural network is to be applied
to. These techniques do nothing to adjust the structure of the neural network.
Chapter 12: Fuzzy Logic
(Wednesday, November 16, 2005)
In this chapter we will examine fuzzy logic. Fuzzy logic is a branch of artificial intelligence
that is not directly related to the neural networks that we have been examining so far.
Fuzzy logic is often used to process data before it is fed to a neural network, or to process
the outputs from the neural network. In this chapter we will examine cases of how this can
be done. We will also look at an example program that uses fuzzy logic to filter incoming
SPAM emails.
Appendix A. JOONE Reference
(Wednesday, November 16, 2005)
Information about JOONE.
Appendix B. Mathematical Background
(Friday, July 22, 2005)
Discusses some of the mathematics used in this book.
Appendix C. Compiling Examples under Windows
(Friday, July 22, 2005)
How to install JOONE and the examples on Windows.
Appendix D. Compiling Examples under Linux/UNIX
http://ifile.it/vhitauq/programming_neural_networks_in_java.pdf
