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Proceedings Paper

Adaptive Dynamic Heteroassociative Neural Memories For Pattern Classification
Author(s): Mohamad H. Hassoun
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Paper 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.

Paper Details

Date Published: 29 June 1989
PDF: 11 pages
Proc. SPIE 1053, Optical Pattern Recognition, (29 June 1989); doi: 10.1117/12.951518
Show Author Affiliations
Mohamad H. Hassoun, Wayne State University (United States)


Published in SPIE Proceedings Vol. 1053:
Optical Pattern Recognition
Hua-Kuang Liu, Editor(s)

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