Summary

Hopfield networks can act as error correction or vector categorization networks. Input vectors are used as the initial conditions to the network, which recurrently updates until it reaches a stable output vector.

Hopfield networks are interesting from a theoretical standpoint, but are seldom used in practice. Even the best Hopfield designs may have spurious stable points that lead to incorrect answers. More efficient and reliable error correction techniques, such as backpropagation, are available.

Functions

This chapter introduces the following functions:

Function	Description
newhop	Create a Hopfield recurrent network.
satlins	Symmetric saturating linear transfer function.

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Hopfield Network

Network Object Reference

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