1999 Fiscal Year Final Research Report Summary
Autonomous systems with explanatory internal models
| Project/Area Number |
10650434
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Control engineering
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| Research Institution | KYUSHU UNIVERSITY |
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Principal Investigator |
MURATA Junichi Kyushu University, Graduate School of Information Science and Electrical Engineering, Associate Professor, 大学院・システム情報科学研究科, 助教授 (60190914)
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| Co-Investigator(Kenkyū-buntansha) |
OHBAYASHI Masanso Yamaguchi University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (60213849)
HU Jinglu Kyushu University, Graduate School of Information Science and Electrical Engineering, Research Associate, 大学院・システム情報科学研究科, 助手 (50294905)
HIRASAWA Kotaro Kyushu University, Graduate School of Information Science and Electrical Engineering, Professor, 大学院・システム情報科学研究科, 教授 (70253474)
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| Project Period (FY) |
1998 – 1999
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| Keywords | Learning / Optimization / Chaos / Symbiosis / Neural networks with gates / RBF networks / Autonomous moving agent / Nonlinear system control |
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| Research Abstract |
Autonomous systems with internal models requires learning algorithms based on variety of trials, methods for extracting general knowledge out of trained results, and hierarchical control structure and its switching mechanisms. To meet these requisites, the following have been studied.
As a tool for learning based on variety of trials, the following have been devised : a control mechanism of chaotic behaviors which can produce non-deterministic trials, an optimization method which controls the search range as required, genetic symbiosis algorithm that finds various(sub-)optimal solutions with desirable features, and an optimization procedure that tunes its own design parameters. Their validity has been examined by examples.
To achieve extraction of general knowledge, the following have been proposed : neural networks with input gates and a method to merge nodes in Radial Basis Function networks. These enables us to represent the knowledge in a generally applicable way which has a fewer number of inputs(conditions). This has been verfied through learning of behaviors of autonomous moving agents.
Neural networks with node gates have been also devised which can realize hierarchical control structure as well as its switching mechanism at the same time. This has been confirmed by examples of nonlinear system control. In addition, in learning of behaviors of autonomous moving agents, a mechanism has been added that extract rules applicable to general environments to are. This makes it possible for the agents to adapt new environment more quickly, which has been demonstrated in examples.
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Research Products
(26 results)
