Adaptive Dynamic Heteroassociative Neural Memories For Pattern Classification

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Abstract
An adaptive dynamic artificial neural memory is proposed for pattern recognition applications. The proposed neural memory has a simple layered structure of neural processing units (neurons) with feedback which is ideal for parallel optical implementations. An adaptive version of our earlier-proposed high-performance neural memory recording algorithm (Ho-Kashyap recording algorithm) is utilized for the memory learning phase. This learning algorithm is computationaly inexpensive and leads to high-performance associative memory characteristics. The combination of this algorithm with a dynamic heteroassociative memory architecture gives rise to high associative memory capabilities which are suitable for adaptive and robust pattern classification applications. The state-space characteristics of dynamic heteroassociative memories (DAMs) utilizing various recording/synthesis algorithms are studied and the advantages of the proposed associative memory over the earlier proposed bidirectional associative memory (BAN) and generalized inverse-recorded heteroassociative memory are established and analyzed.

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