DocumentCode :
315419
Title :
Multi-module network associating patterns and symbols with attention mechanism
Author :
Hattori, Yoichiro ; Furuhashi, Takeshi ; Uchikawa, Yoshiki
Author_Institution :
Dept. of Inf. Electron., Nagoya Univ., Japan
Volume :
1
fYear :
1997
fDate :
27-23 May 1997
Firstpage :
148
Abstract :
The authors present a new network which consists of symbol layers and a pattern layer for acquiring concepts and inferring meanings of patterns and symbols. Nonlinear dynamics which can cause chaotic vibration in the internal state of the network is used for the recollection of various related patterns and symbols. The authors also propose a multi-module network with the multiple symbol-pattern networks for association and inference from vague patterns with multiple meanings. This paper presents an attention mechanism for this model. This new mechanism works to control the search area for related patterns and symbols. Simulation using face patterns consisting of eyebrow, eye and mouth patterns are done to show that the attention mechanism works well to recall related facial expressions successively
Keywords :
associative processing; content-addressable storage; feedforward neural nets; inference mechanisms; learning (artificial intelligence); multilayer perceptrons; pattern recognition; association; attention mechanism; chaotic vibration; face patterns; facial expressions; inference; learning; multimodule network; multiple symbol-pattern networks; nonlinear dynamics; nultilayer neural network; patterns; search; simulation; symbols; vague patterns; Associative memory; Chaos; Computational intelligence; Computational modeling; Computer networks; Eyebrows; Eyes; Information processing; Intelligent systems; Mouth;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Knowledge-Based Intelligent Electronic Systems, 1997. KES '97. Proceedings., 1997 First International Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-3755-7
Type :
conf
DOI :
10.1109/KES.1997.616878
Filename :
616878
Link To Document :
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