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Neural Networks are one of the most commonly used systems in Artificial Intelligence. Neural Networks are particularly adept at recognizing patterns. This allows them to recognize something, even when distorted.

A Neural Network may have input, output and hidden layers. The input and output layers are the only required layers. The input and output layer may be the same neurons. Neural networks are typically presented input patterns that will produce some output pattern.

If a Neural network mimics the input pattern it was presented with, then that network is said to be autoassociative. For example, if a neural network were presented with the pattern “0110”, and the output were also “0110”, then that network would be said to be autoassociative.

A neural network calculates its output based on the input pattern and the neural network’s internal connection weight matrix. The values for these connection weights will determine the output from the neural network, based upon input pattern.

A Hopfield neural network is a fully connected autoassociative neural network. What this means, is that each neuron is connected to every other neuron in a Hopfield Neural Network. A Hopfield Neural Network can be trained to recognize certain patterns. Training a Hopfield Neural Network involves performing some basic matrix manipulations on the input pattern that is to be recognized.

This chapter showed how to construct a simple Hopfield Neural Network. The next chapter will show how to create a multilayered neural network. To do this, you will be introduced to the JOONE package that is freely available for Java.