| ID | 30121 |
| フルテキストURL | |
| 著者 | |
| 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
|
| 巻 | 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
|
