Hybrid Quaternionic Hopfield Neural Network

• KOBAYASHI Masaki

  Interdisciplinary Graduate School, University of Yamanashi

抄録

In recent years, applications of complex-valued neural networks have become wide spread. Quaternions are an extension of complex numbers, and neural networks with quaternions have been proposed. Because quaternion algebra is non-commutative algebra, we can consider two orders of multiplication to calculate weighted input. However, both orders provide almost the same performance. We propose hybrid quaternionic Hopfield neural networks, which have both orders of multiplication. Using computer simulations, we show that these networks outperformed conventional quaternionic Hopfield neural networks in noise tolerance. We discuss why hybrid quaternionic Hopfield neural networks improve noise tolerance from the standpoint of rotational invariance.

収録刊行物

• IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

  IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E98.A (7), 1512-1518, 2015

  一般社団法人 電子情報通信学会

キーワード

• Hopfield neural networks
• complex-valued associative memory
• quaternion
• rotational invariance

詳細情報

• CRID: 1390282681287636352
• NII論文ID: 130005085803
• DOI: 10.1587/transfun.e98.a.1512
• ISSN: 17451337
• ISSN: 09168508
• Web Site: https://www.jstage.jst.go.jp/article/transfun/E98.A/7/E98.A_1512/_pdf
• 本文言語コード: en
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