Introduction :: Perceptrons (Neural Network Toolbox™)

Introduction

This chapter has a number of objectives. First, it introduces you to learning rules, methods of deriving the next changes that might be made in a network, and training, a procedure whereby a network is actually adjusted to do a particular job. Along the way, this chapter describes a toolbox function to create a simple perceptron network and functions to initialize and simulate such networks. The perceptron is used as a vehicle for tying these concepts together.

Rosenblatt [Rose61] created many variations of the perceptron. One of the simplest was a single-layer network whose weights and biases could be trained to produce a correct target vector when presented with the corresponding input vector. The training technique used is called the perceptron learning rule. The perceptron generated great interest due to its ability to generalize from its training vectors and learn from initially randomly distributed connections. Perceptrons are especially suited for simple problems in pattern classification. They are fast and reliable networks for the problems they can solve. In addition, an understanding of the operations of the perceptron provides a good basis for understanding more complex networks.

This chapter defines what is meant by a learning rule, explains the perceptron network and its learning rule, and tells you how to initialize and simulate perceptron networks.

The discussion of perceptrons in this chapter is necessarily brief. For a more thorough discussion, see Chapter 4, "Perceptron Learning Rule," of [HDB1996], which discusses the use of multiple layers of perceptrons to solve more difficult problems beyond the capability of one layer.

You might also want to refer to the original book on the perceptron, Rosenblatt, F., Principles of Neurodynamics, Washington D.C., Spartan Press, 1961 [Rose61].

Important Perceptron Functions

You can create perceptron networks with the function newp. These networks can be initialized, simulated, and trained with init, sim, and train. Neuron Model describes how perceptrons work and introduces these functions.

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Perceptrons Neuron Model

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