Learning algorithms for auto-associators with full storage capacity

Two learning algorithms for storing data in an autoassociator with full storage capacity are presented. The first algorithm can teach a neural network to store short-bit-length data with full storage capacity (as compared to the Hebb rule), and the second algorithm can do the same for long-bit-length data at a higher speed (as compared to the delta rule). The algorithms eliminate the problem of limited storage capacity associated with the Hebb rule, and their learning speeds are much faster than that of the corresponding delta rule. Simulations have been done to compare the two learning algorithms to those of the Hebb rule and the delta rule. Results show that the two algorithms are much better than the Hebb rule in terms of storage capacity and much better than the delta rule in terms of learning speed