MULTIPLE TRAINING CONCEPT FOR BACKPROPAGATION NEURAL NETWORKS FOR USEIN ASSOCIATIVE MEMORIES

The multiple training concept first applied to Bidirectional Associati ve Memory training is applied to the back-propagation (BP) algorithm f or use in associative memories. This new algorithm, which assigns diff erent weights to the various pairs in the energy function, is called m ultiple training back-propagation (MTBP). The pair weights are updated during the training phase using the basic differential multiplier met hod (BDMM). A sufficient condition for convergence of the training pha se is that the second derivative of the energy function with respect t o the weights of the synapses is positive along the paths of both syna pse weights and pair weights. A simple example of the use of the algor ithm is provided, followed by two simulations that show that this algo rithm can increase the training speed of the network dramatically.