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Multiple training concept for back-propagation neural networks for use in associative memories

✍ Scribed by Yeou-Fang Wang; Jose B. Cruz Jr.; J.H. Mulligan Jr.

Publisher: Elsevier Science
Year: 1993
Tongue: English
Weight: 517 KB
Volume: 6
Category: Article
ISSN: 0893-6080
DOI: 10.1016/s0893-6080(09)80027-0

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✦ Synopsis

The multipletraining concept first appliedto Bidirectional Associative Memory trainingis appliedto the back-propagation (BP) algorithm for use in associative memories. This new algorithm. which assigns different weights to the various pairsin the energyfunction, is calledmultiple training back-propagation (MTBP). The pair weightsare updatedduring the trainingphase using the basic differential multiplier method (BDMM). A sufficient condition for convergence of the trainingphase is that the secondderivative of the energyfunction with respect to the weights ofthe synapses is positive alongthe paths of both synapse weights and pair weights. A simple example ofthe use ofthe algorithm is provided, followed by two simulations that show that this algorithm can increase the training speed ofthe network dramatically.

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