The Dynamical Recollection of Interconnected Neural Networks Using Meta-heuristics
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- Kuremoto Takashi
- Graduate School of Science and Engineering, Yamaguchi University
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- Watanabe Shun
- Graduate School of Science and Engineering, Yamaguchi University
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- Kobayashi Kunikazu
- Graduate School of Science and Engineering, Yamaguchi University
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- Feng Laing-Bing
- Graduate School of Science and Engineering, Yamaguchi University
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- Obayashi Masanao
- Graduate School of Science and Engineering, Yamaguchi University
Bibliographic Information
- Other Title
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- 相互結合型ネットワークにおけるメタヒューリスティクスを用いた動的想起
- ソウゴ ケツゴウガタ ネットワーク ニ オケル メタヒューリスティクス オ モチイタ ドウテキ ソウキ
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Abstract
The interconnected recurrent neural networks are well-known with their abilities of associative memory of characteristic patterns. For example, the traditional Hopfield network (HN) can recall stored pattern stably, meanwhile, Aihara's chaotic neural network (CNN) is able to realize dynamical recollection of a sequence of patterns. In this paper, we propose to use meta-heuristic (MH) methods such as the particle swarm optimization (PSO) and the genetic algorithm (GA) to improve traditional associative memory systems. Using PSO or GA, for CNN, optimal parameters are found to accelerate the recollection process and raise the rate of successful recollection, and for HN, optimized bias current is calculated to improve the network with dynamical association of a series of patterns. Simulation results of binary pattern association showed effectiveness of the proposed methods.
Journal
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- IEEJ Transactions on Electronics, Information and Systems
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IEEJ Transactions on Electronics, Information and Systems 131 (8), 1475-1484, 2011
The Institute of Electrical Engineers of Japan
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Details 詳細情報について
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- CRID
- 1390282679584459776
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- NII Article ID
- 10030527208
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- NII Book ID
- AN10065950
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- BIBCODE
- 2011ITEIS.131.1475K
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- ISSN
- 13488155
- 03854221
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- NDL BIB ID
- 11196124
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed