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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/5026
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| タイトル: | An agent-based approach to identification of prediction models |
| 著者: | Ma, Tieju
Nakamori, Yoshiteru |
| キーワード: | agent
local linear model
global model
data space |
| 発行日: | 2003-08 |
| 出版者: | World Scientific Publishing |
| 誌名: | International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems |
| 巻: | 11 |
| 号: | 4 |
| 開始ページ: | 495 |
| 終了ページ: | 514 |
| DOI: | 10.1142/S021848850300220X |
| 抄録: | This paper presents an agent-based approach to identification of prediction models in two-dimensional data spaces. A number of agents are sent to the two-dimensional data space that people want to investigate. At the micro-level, every agent tries to build a local linear model by competing with others, and then at the macro-level all surviving agents build the global model by cooperating with each other. And a genetic algorithm is introduced for improving the global model built by the agents. Two examples that apply this approach are given. The advantages of this approach are it does not need people to give a certain formula in advance; and most of time, it can give more precise prediction models than those given by traditional methods. |
| Rights: | Electronic version of an article published as International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(4), 2003, 495-514. DOI:10.1142/S021848850300220X. Copyright World Scientific Publishing Company, http://dx.doi.org/10.1142/S021848850300220X |
| URI: | http://hdl.handle.net/10119/5026 |
| 資料タイプ: | author |
| 出現コレクション: | a10-1. 雑誌掲載論文 (Journal Articles)
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このアイテムのファイル:
| ファイル |
記述 |
サイズ | 形式 |
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| C10107.pdf | | 551Kb | Adobe PDF | 見る/開く |
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