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"subitem_description_type": "Other"}, {"subitem_description": "\u7814\u7a76\u6982\u8981\uff08\u82f1\u6587\uff09 : The main objectives of this research project are to construct emotional neural networks and apply them in robot control. In this project, we develop spiking neural networks (SNNs) using genetic algorithms (GA). In a previous study, we developed emotional neural networks using a model of an analog neuron, by computer simulation. However, neural networks of living organisms are based on electrical spikes. It is said that information processing in the brain occurs through neuron activation by neural spikes. Therefore a model of an emotional neural network should also be based on neuron activation by spikes. In engineering fields, many training methods for artificial neural networks have been reported. However, there are few reports concerning SNNs. In this study, we present a training method for SNNs using GA. The back-propagation (BP) method, which is a very popular and powerful training method, cannot be easily applied to SNN training because spiking of a neuron is a discontinuous neural activity function. The GA method can be used to successfully train the SNN independently of the mode of the activity function. In the simulation study, we confirmed that the GA method is suitable for SNN training and that SNNs can be easily applied as emotional neural networks. The application of emotional neural networks based on the SNN model to robot control is left for future work.", "subitem_description_type": "Other"}, {"subitem_description": "\u672a\u516c\u958b\uff1aP.3\u4ee5\u964d\uff08\u5225\u5237\u8ad6\u6587\uff09", "subitem_description_type": "Other"}, {"subitem_description": "\u7814\u7a76\u5831\u544a\u66f8", "subitem_description_type": "Other"}]}, "item_1617186643794": {"attribute_name": "Publisher", "attribute_value_mlt": [{"subitem_1522300295150": "ja", "subitem_1522300316516": "\u91d1\u57ce\u5bdb"}]}, "item_1617186702042": {"attribute_name": "Language", "attribute_value_mlt": [{"subitem_1551255818386": "jpn"}]}, "item_1617186783814": {"attribute_name": "Identifier", "attribute_value_mlt": [{"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/20.500.12000/11407"}]}, "item_1617186920753": {"attribute_name": "Source Identifier", "attribute_value_mlt": [{"subitem_1522646500366": "NCID", "subitem_1522646572813": "BA64351541"}]}, "item_1617258105262": {"attribute_name": "Resource Type", "attribute_value_mlt": [{"resourcetype": "research report", "resourceuri": "http://purl.org/coar/resource_type/c_18ws"}]}, "item_1617265215918": {"attribute_name": "Version Type", "attribute_value_mlt": [{"subitem_1522305645492": "VoR", "subitem_1600292170262": "http://purl.org/coar/version/c_970fb48d4fbd8a85"}]}, "item_1617605131499": {"attribute_name": "File", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "download_preview_message": "", "file_order": 0, "filename": "12650411.pdf", "future_date_message": "", "is_thumbnail": false, "mimetype": "", "size": 0, "url": {"objectType": "fulltext", "url": "https://u-ryukyu.repo.nii.ac.jp/record/2004733/files/12650411.pdf"}, "version_id": "d46249e2-3ae1-4a61-b426-da6db18608a9"}]}, "item_title": "\u30b9\u30c8\u30ec\u30b9\u523a\u6fc0\u306b\u53cd\u5fdc\u3059\u308b\u60c5\u52d5\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u3092\u7528\u3044\u305f\u81ea\u5f8b\u79fb\u52d5\u30ed\u30dc\u30c3\u30c8\u306e\u884c\u52d5\u5236\u5fa1", "item_type_id": "15", "owner": "1", "path": ["1642838403123", "1642838406845"], "permalink_uri": "http://hdl.handle.net/20.500.12000/11407", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2009-07-23"}, "publish_date": "2009-07-23", "publish_status": "0", "recid": "2004733", "relation": {}, "relation_version_is_last": true, "title": ["\u30b9\u30c8\u30ec\u30b9\u523a\u6fc0\u306b\u53cd\u5fdc\u3059\u308b\u60c5\u52d5\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u3092\u7528\u3044\u305f\u81ea\u5f8b\u79fb\u52d5\u30ed\u30dc\u30c3\u30c8\u306e\u884c\u52d5\u5236\u5fa1"], "weko_shared_id": -1}
  1. その他
  1. 部局別インデックス
  2. 工学部

ストレス刺激に反応する情動ニューラルネットを用いた自律移動ロボットの行動制御

http://hdl.handle.net/20.500.12000/11407
http://hdl.handle.net/20.500.12000/11407
4d7d80a3-0c0b-4786-b63a-019c2cf5bafd
名前 / ファイル ライセンス アクション
12650411.pdf 12650411.pdf
Item type デフォルトアイテムタイプ(フル)(1)
公開日 2009-07-23
タイトル
タイトル ストレス刺激に反応する情動ニューラルネットを用いた自律移動ロボットの行動制御
言語 ja
作成者 金城, 寛

× 金城, 寛

ja 金城, 寛

山本, 哲彦

× 山本, 哲彦

ja 山本, 哲彦

中園, 邦彦

× 中園, 邦彦

ja 中園, 邦彦

Kinjo, Hiroshi

× Kinjo, Hiroshi

en Kinjo, Hiroshi

Yamamoto, Tetsuhiko

× Yamamoto, Tetsuhiko

en Yamamoto, Tetsuhiko

Nakazono, Kunihiko

× Nakazono, Kunihiko

en Nakazono, Kunihiko

アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
主題
言語 ja
主題Scheme Other
主題 スパイキングニューラルネット
言語 ja
主題Scheme Other
主題 行動制御
言語 ja
主題Scheme Other
主題 情動情報処理
言語 ja
主題Scheme Other
主題 移動ロボット
言語 ja
主題Scheme Other
主題 自律適応システム
言語 ja
主題Scheme Other
主題 ニューラルネット
言語 ja
主題Scheme Other
主題 遺伝的アルゴリズム
内容記述
内容記述タイプ Other
内容記述 科研費番号: 12650411
内容記述タイプ Other
内容記述 平成12年~13年度科学研究費補助金(基盤研究(C)(2))研究成果報告書
内容記述タイプ Other
内容記述 研究概要 : 本研究の主たるテーマは、情動情報を扱うニューラルネットを構築し、それを移動ロボットの行動制御へ適用しようとするものであった。それは、生物の行動が、喜び、安堵、怒り、恐怖、など情動情報に左右されるからであり、自然な発想に基づいたものであった。しかしながら研究を進めていくと、生物のニューロンの仕組みがこれまで想定していたものと違っていることに気がついた。それは、生物のニューロンは電気的スパイク列で情報処理を行っており、情動ニューラルネットについても、スパイク列を扱うスパイキングニューラルネットでなければ真に生物の情動情報処理を模擬しているとは言えないのではないかということだ。そこで、研究方針を変更し、アナログ信号を扱う従来のニューラルネットで情動ニューラルネットを構築することを止め、スパイク信号を扱うスパイキングニューラルネットで情動ニューラルネットを構築する研究へと方針転換を行なった。方針転換の時期が遅れたこともあり、最終的にスパイキングニューラルネットで情動ニューラルネットを構築することはできなかった。しかしながら、本研究により、スパイキングニューラルネットの挙動解析を行なうことができた。また、スパイキングニューラルネットに対して、これまでにない新たなニューラルネットの学習方法の提案を行なうことができた。それは、遺伝的アルゴリズムを用いてスパイキングニューラルネットを学習する方法であり、シミュレーション実験のみであるがその有効性を確認している。スパイキングニューラルネットは、今後のニューラルネット研究の大きな柱であり、情動ニューラルネットをはじめ、多くの応用の可能性を秘めている。科学研究費補助金の研究期間は終了したが、今後もこの方針で研究を進めたい。
内容記述タイプ Other
内容記述 研究概要(英文) : The main objectives of this research project are to construct emotional neural networks and apply them in robot control. In this project, we develop spiking neural networks (SNNs) using genetic algorithms (GA). In a previous study, we developed emotional neural networks using a model of an analog neuron, by computer simulation. However, neural networks of living organisms are based on electrical spikes. It is said that information processing in the brain occurs through neuron activation by neural spikes. Therefore a model of an emotional neural network should also be based on neuron activation by spikes. In engineering fields, many training methods for artificial neural networks have been reported. However, there are few reports concerning SNNs. In this study, we present a training method for SNNs using GA. The back-propagation (BP) method, which is a very popular and powerful training method, cannot be easily applied to SNN training because spiking of a neuron is a discontinuous neural activity function. The GA method can be used to successfully train the SNN independently of the mode of the activity function. In the simulation study, we confirmed that the GA method is suitable for SNN training and that SNNs can be easily applied as emotional neural networks. The application of emotional neural networks based on the SNN model to robot control is left for future work.
内容記述タイプ Other
内容記述 未公開:P.3以降(別刷論文)
内容記述タイプ Other
内容記述 研究報告書
出版者
言語 ja
出版者 金城寛
言語
言語 jpn
資源タイプ
資源タイプ research report
資源タイプ識別子 http://purl.org/coar/resource_type/c_18ws
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
識別子
識別子 http://hdl.handle.net/20.500.12000/11407
識別子タイプ HDL
収録物識別子
収録物識別子タイプ NCID
収録物識別子 BA64351541
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