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- Hoshino Yuki
- Graduate School of Nippon Institute of Technology
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- Jin'no Kenya
- Tokyo City University
抄録
Recently, machine learning has been attracting attention. Machine learning is mainly realized by the learning of artificial neural networks. Various learning methods have been proposed; however, the learning methods are based on gradient methods. On the other hand, swarm intelligence (SI) algorithms have been attracting attention in the optimization field. Generally speaking, SI algorithms have a large computation cost. Therefore, there are few cases of SI algorithms being applied to machine learning. In this paper, we propose a novel learning algorithm for an artificial neural network which applies our proposed nonlinear map optimization (NMO) method. NMO consists of some simple particles which are driven by a simple nonlinear map. NMO can be classified as an SI algorithm. However, it has only a small computation cost. Therefore, NMO can be applied to a learning algorithm for an artificial neural network. In this paper, we introduce NMO, and a small learning simulation is carried out to confirm the performance of our learning method.
収録刊行物
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- 信号処理
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信号処理 22 (4), 153-156, 2018-07-25
信号処理学会
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詳細情報 詳細情報について
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- CRID
- 1390564238001074816
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- NII論文ID
- 130007418562
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- ISSN
- 18801013
- 13426230
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可