From Bidirectional Associative Memory to a noise-tolerant, robust Protein Processor Associative Memory

Authors:
Omer Qadir;Jerry Liu;Gianluca Tempesti;Jon Timmis;Andy Tyrrell
Affiliations:
Department of Electronics, University of York, Heslington, YO10 5DD, York, UK;Department of Electronics, University of York, Heslington, YO10 5DD, York, UK;Department of Electronics, University of York, Heslington, YO10 5DD, York, UK;Department of Electronics, University of York, Heslington, YO10 5DD, York, UK;Department of Electronics, University of York, Heslington, YO10 5DD, York, UK
Venue:
Artificial Intelligence
Year:
2011

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Abstract

Protein Processor Associative Memory (PPAM) is a novel architecture for learning associations incrementally and online and performing fast, reliable, scalable hetero-associative recall. This paper presents a comparison of the PPAM with the Bidirectional Associative Memory (BAM), both with Kosko's original training algorithm and also with the more popular Pseudo-Relaxation Learning Algorithm for BAM (PRLAB). It also compares the PPAM with a more recent associative memory architecture called SOIAM. Results of training for object-avoidance are presented from simulations using player/stage and are verified by actual implementations on the E-Puck mobile robot. Finally, we show how the PPAM is capable of achieving an increase in performance without using the typical weighted-sum arithmetic operations or indeed any arithmetic operations.