{"created":"2025-01-18T22:47:37.159501+00:00","updated":"2025-01-23T01:10:55.085488+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00013567","sets":["581:742:745"]},"path":["745"],"owner":"1","recid":"13567","title":["マキシマムニューロンを用いたN - Queen問題のニューラルネット解法の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"1996-10-15"},"_buckets":{"deposit":"e9a13098-0f87-4290-992c-f87ce46a0eb7"},"_deposit":{"id":"13567","pid":{"type":"depid","value":"13567","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"マキシマムニューロンを用いたN - Queen問題のニューラルネット解法の提案","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"マキシマムニューロンを用いたN - Queen問題のニューラルネット解法の提案"},{"subitem_title":"Maximum Neural Network Algorithms for N - Queen Problems","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"論文","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"1996-10-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"大阪大学基礎工学部"},{"subitem_text_value":"大阪大学基礎工学部"},{"subitem_text_value":"大阪大学基礎工学部"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Faculity of Engineering Science, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Faculity of Engineering Science, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Faculity of Engineering Science, Osaka University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/13567/files/IPSJ-JNL3710005.pdf"},"date":[{"dateType":"Available","dateValue":"1998-10-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL3710005.pdf","filesize":[{"value":"600.4 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"28d54749-a2fa-4cf5-9e16-a32e995372d0","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 1996 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"竹中, 要一"},{"creatorName":"船曳, 信生"},{"creatorName":"西川, 清史"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yoichi, Takenaka","creatorNameLang":"en"},{"creatorName":"Nobuo, Funabiki","creatorNameLang":"en"},{"creatorName":"Seishi, Nishikawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本論文では  マキシマムニューロンを用いたN-Queen問題の解法を提案する. マキシマムニューロンは  ニューラルネットワークによる組合せ最適化問題の効率的解法を目的として  Takefujiらによって提案されたニューロンモデルである. マキシマムニューロンでは  解空間を構成するニューロンをグループに分割し  各グループ内でただ1つのニューロンのみが発火する. これにより  探索空間の大幅な縮小  ニューロン状態更新に必要な計算量の減少を実現している. 本論文では  シミュレーションにより  マキシマムニューロンによる解法が  従来のニューラルネットワーク解法より優れた求解性能を有することを示す. 特に  本解法が準同期式並列計算に非常に適した方法であることを明らかにする.","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This paper presents maximum neural network algorithms for N-queen problems using the maximum neuron model. The maximum neuron model is proposed by Takefuji et al. in order to provide efficient neural network solutions for combinatorial optimization problems. In this model, one and only one neuron id always fired in each group of neurons, which can not only reduce the searching space drastically but also save the computation load. The simulation results show that the performance of our maximum neural network surpasses the existing neural network for the same problem. Particularly, the maximum neural network in shown to be much suitable for the semi-synchronous computation.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1788","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"1781","bibliographicIssueDates":{"bibliographicIssueDate":"1996-10-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"37"}]},"relation_version_is_last":true,"item_2_alternative_title_2":{"attribute_name":"その他タイトル","attribute_value_mlt":[{"subitem_alternative_title":"人工知能"}]},"weko_creator_id":"1"},"id":13567,"links":{}}