Title | ||
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From Bidirectional Associative Memory to a noise-tolerant, robust Protein Processor Associative Memory |
Abstract | ||
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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. |
Year | DOI | Venue |
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2011 | 10.1016/j.artint.2010.10.008 | Artif. Intell. |
Keywords | Field | DocType |
novel architecture,recent associative memory architecture,associations incrementally,e-puck mobile robot,typical weighted-sum arithmetic operation,bidirectional associative memory,protein processor associative memory,actual implementation,original training algorithm,arithmetic operation,robust protein processor associative,associative memory,mobile robot | Content-addressable memory,Computer science,Bidirectional associative memory,Artificial intelligence,Memory map,Artificial neural network,Recall,Machine learning,Memory architecture,Mobile robot,Scalability | Journal |
Volume | Issue | ISSN |
175 | 2 | 0004-3702 |
Citations | PageRank | References |
8 | 0.53 | 16 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Omer Qadir | 1 | 11 | 1.60 |
Jerry Liu | 2 | 82 | 8.65 |
Gianluca Tempesti | 3 | 457 | 57.09 |
Jon Timmis | 4 | 1237 | 120.32 |
Andy Tyrrell | 5 | 158 | 13.74 |