P pattern recognition based on a probabilistic RAM net using n-tuple input mapping

A multilayer digital neural network, based on the probabilistic random access memory (pRAM), is used as a P pattern classifier system. This network presents an elaborate implementation of the n-tuple technique, which has mostly been used for pattern recognition (Bledsoe and Browning, 1959). The network´s main properties, discrimination and generalisation, are discussed as a function of the pRAM connectivity. Pyramid networks, based on different pRAM connectivities, are simulated using an enhanced version of global reinforcement learning. n-tuple input mapping based on data analysis is proposed. The results show that combining the permuted data-based input mapping with a pRAM net, using different node connectivities through the pyramid layers, can achieve a good balance of the network´s properties, when handling a P pattern classification task. Results are presented for the 10 digit recognition problem, which are motivating and very encouraging