Neural Network Toolbox

Summary

Elman networks, by having an internal feedback loop, are capable of learning to detect and generate temporal patterns. This makes Elman networks useful in such areas as signal processing and prediction where time plays a dominant role.

Because Elman networks are an extension of the two-layer sigmoid/linear architecture, they inherit the ability to fit any input/output function with a finite number of discontinuities. They are also able to fit temporal patterns, but may need many neurons in the recurrent layer to fit a complex function.

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.

Design (newhop)

Figures

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