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ID 30121
フルテキストURL
著者
Handa, Hisashi Okayama University Kaken ID publons researchmap
抄録

We propose herein a new incremental state construction method which consists of Fritzke's growing neural gas algorithm and a class management mechanism of GNG units. The GNG algorithm condenses sensory inputs and learns which areas are frequently sensed. The CMM yields a new state based upon the anticipated behaviors of the agent, i.e., a couple of actions by an agent and the resultant change in sensory inputs. Computational simulations on the mountain-car task confirm the effectiveness of the proposed method.

キーワード
learning (artificial intelligence)
neural nets
state-space methods
備考
Digital Object Identifier: 10.1109/IJCNN.2004.1380090
Published with permission from the copyright holder. This is the institute's copy, as published in Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on, 25-29 July 2004, Vol. 2, Pages 1115-1120.
Publisher URL:http://dx.doi.org/10.1109/IJCNN.2004.1380090
Copyright © 2004 IEEE. All rights reserved.
発行日
2004-7
出版物タイトル
Neural Networks
2巻
開始ページ
1115
終了ページ
1120
資料タイプ
学術雑誌論文
言語
英語
査読
有り
DOI
Submission Path
industrial_engineering/20