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

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'...

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Bibliographic Details
Published in:Artificial intelligence 2011-02, Vol.175 (2), p.673-693
Main Authors: Qadir, Omer, Liu, Jerry, Tempesti, Gianluca, Timmis, Jon, Tyrrell, Andy
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Arithmetic
Artificial intelligence
Associative Memory
BAM
Computer science
control theory
systems
Connectionism. Neural networks
Control theory. Systems
Exact sciences and technology
Hetero-associative
Learning
Microprocessors
Mobile robotics
PRLAB
Protein processing
Proteins
Robotics
Robots
SABRE
Self-organising
Self-regulating
SOIAM
Training
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Summary: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.
ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2010.10.008