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Pattern recognition is one of the most common uses for neural networks. Pattern recognition is simply the ability to recognize a pattern. The pattern must be recognized even when that pattern is distorted. Consider an every day use of pattern recognition.

Every person who holds a driver’s license should be able to accurately identify a traffic light. This is an extremely critical pattern recognition procedure carried out by countless drivers every day. But not every traffic light looks the same. Even the same traffic light can be altered depending on the time of day or the season. In addition, many variations of the traffic light exist. This is not a hard task for a human driver.

How hard would it be to write a computer program that accepts an image and tells you if it is a traffic light? This would be a very complex task. Figure 2.3 shows several such lights. Most common programming algorithms are quickly exhausted when presented with a complex pattern recognition problem.

Figure 2.3: Different Traffic Lights

Recognizing patterns is what neural networks do best. This chapter teaches you how to create a very simple neural network that is capable of only the most basic pattern recognition. The neural network built in this chapter will not recognize traffic lights. In our study of neural networks we will begin simple. This chapter will focus on recognizing very simple 4-digit binary sequences, such as 0101 and 1010. Not every example in the book will be so simple. Later chapters will focus on more complex image recognition. Before you can construct a neural network, you must first be shown how a neural network actually recognizes an image. We’ve already seen the basic structure of a neural network.