Projects per year
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. (C) 2010 Elsevier B.V. All rights reserved.
Original language | English |
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Pages (from-to) | 673-693 |
Number of pages | 21 |
Journal | Artificial Intelligence |
Volume | 175 |
Issue number | 2 |
DOIs | |
Publication status | Published - Feb 2011 |
Keywords
- Self-organising
- Self-regulating
- Associative Memory
- Protein processing
- Hetero-associative
- BAM
- PRLAB
- SOIAM
- SABRE
- Mobile robotics
- NEURAL-NETWORKS
- PERFORMANCE
- CIRCUITS
- STRATEGY
Projects
- 1 Finished
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SABRE: Self-healing Cellular Architectures
Tyrrell, A., Liu, J., Qadir, O., Tempesti, G. & Timmis, J.
1/10/08 → 30/09/11
Project: Research project (funded) › Research