Introduction

This chapter covers networks that are of historical interest, but that are not as actively used today as networks presented in earlier chapters. Two of the networks are single-layer networks that were the first neural networks for which practical training algorithms were developed: perceptron networks and ADALINE networks. This chapter also covers recurrent Hopfield networks.

The perceptron network is single-layer network whose weights and biases can be trained to produce a correct target vector when presented with the corresponding input vector. This perceptron rule was the first training algorithm developed for neural networks. The original book on the perceptron is Rosenblatt, F., Principles of Neurodynamics, Washington D.C., Spartan Press, 1961 [Rose61].

At about the same time that Rosenblatt developed the perceptron network, Widrow and Hoff developed a single-layer linear network and associated learning rule, which they called the ADALINE (Adaptive Linear Neuron). This network was used to implement adaptive filters, which are still actively used today. The original paper describing this network is Widrow, B., and M.E. Hoff, "Adaptive switching circuits," 1960 IRE WESCON Convention Record, New York IRE, 1960, pp. 96–104.

The Hopfield network is used to store one or more stable target vectors. These stable vectors can be viewed as memories that the network recalls when provided with similar vectors that act as a cue to the network memory. You might want to peruse a basic paper in this field:

Li, J., A.N. Michel, and W. Porod, "Analysis and synthesis of a class of neural networks: linear systems operating on a closed hypercube," IEEE Transactions on Circuits and Systems, Vol. 36, No. 11, November 1989, pp. 1405–1422.

Historical Networks

Perceptron Networks

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