Shapes of Non-monotonous Activation Functions in Chaotic Neural Network Associative Memory Model and Its Evaluation
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- Obayashi Masanao
- Graduate School of Sci. and Eng., Yamaguchi Univ.
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- Omiya Rie
- Graduate School of Sci. and Eng., Yamaguchi Univ.
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- Kuremoto Takashi
- Graduate School of Sci. and Eng., Yamaguchi Univ.
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- Kobayashi Kunikazu
- Graduate School of Sci. and Eng., Yamaguchi Univ.
Bibliographic Information
- Other Title
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- カオスニューラルネットワーク連想記憶モデルにおける活性化関数の形状とその評価
- カオスニューラル ネットワーク レンソウ キオク モデル ニ オケル カッセイカ カンスウ ノ ケイジョウ ト ソノ ヒョウカ
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Abstract
The purpose of this paper is to investigate the performance of the associative memory model using Aihara's chaotic neural network with different activation functions. Sigmoid function, a monotonous function, was used in Aihara's original model. However, in the static associative memory, it is reported that the storage capacity of the network is improved when a non-monotonous function is used as the activation function. To improve the associative ability of chaotic neural network, kinds of non-monotonous functions have been proposed to serve as activation function. This paper investigates their difference as to retrieval ability, and proposes an advanced non-monotonous function. By computer simulation, we discuss about what kind of shape is good to improve the associative ability of chaotic neural network.
Journal
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- IEEJ Transactions on Electronics, Information and Systems
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IEEJ Transactions on Electronics, Information and Systems 126 (11), 1401-1405, 2006
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
- 1390001204603985280
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- NII Article ID
- 10018318461
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- NII Book ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL BIB ID
- 8560480
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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
