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          <dc:title>遺伝的アルゴリズムを援用した構造物の振動応答予測モデル構築</dc:title>
          <dc:title xml:lang="en">Genetic Algorithm Aided Modeling for Structures’Vibration Response Prediction</dc:title>
          <jpcoar:creator>
            <jpcoar:creatorName>堀井, 宏祐</jpcoar:creatorName>
            <jpcoar:creatorName>三木, 光範</jpcoar:creatorName>
            <jpcoar:creatorName>小泉, 孝之</jpcoar:creatorName>
            <jpcoar:creatorName>辻内伸好</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Hirosuke, Horii</jpcoar:creatorName>
            <jpcoar:creatorName xml:lang="en">Mitsunori, Miki</jpcoar:creatorName>
            <jpcoar:creatorName xml:lang="en">Takayuki, Koizumi</jpcoar:creatorName>
            <jpcoar:creatorName xml:lang="en">Nobutaka, Tsujiuchi</jpcoar:creatorName>
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          <jpcoar:subject subjectScheme="Other">オリジナル論文</jpcoar:subject>
          <datacite:description descriptionType="Other">本論文では，遺伝的アルゴリズム（GA）を援用した，統計的エネルギー解析法（SEA）による構造物の振動応答予測モデル構築法を提案する．SEA は構造物の振動応答の予測手法の1 つであり，GA はSEA 予測モデルを構成するSEA パラメータ同定に適用される．提案手法はテスト構造物と実建造物に対する振動応答予測によって評価された．その結果，GA によって同定されたパラメータによって構成された予測モデルは，従来の理論式によって構築された予測モデルよりも高精度の予測結果を得た．また，従来の実験的に予測モデルを構築する手法と比較して，加振試験に要する負荷の低減が得られた．</datacite:description>
          <datacite:description descriptionType="Other">In this paper, a genetic algorithm (GA) aided modeling for structures’ vibration response prediction by the statistical energy analysis (SEA) is proposed. The SEA is a prediction method of structurers’ vibration response. The GA is applied to identify the SEA parameters, which constitute the SEA prediction model. The proposed method was evaluated by vibration response prediction of test structures and a real structure. As the result, the SEA prediction model which consisted of parameters identified by the GA obtained more precise prediction result than the current SEA prediction model constructed by theoretical formulas. Furthermore, the loads of experiments for constructing the prediction model are reduced than those of current experimental approach.</datacite:description>
          <dc:publisher xml:lang="ja">情報処理学会</dc:publisher>
          <datacite:date dateType="Issued">2005-01-15</datacite:date>
          <dc:language>jpn</dc:language>
          <dc:type rdf:resource="http://purl.org/coar/resource_type/c_6501">journal article</dc:type>
          <jpcoar:identifier identifierType="URI">https://ipsj.ixsq.nii.ac.jp/records/17220</jpcoar:identifier>
          <jpcoar:sourceIdentifier identifierType="ISSN">1882-7780</jpcoar:sourceIdentifier>
          <jpcoar:sourceIdentifier identifierType="NCID">AA11464803</jpcoar:sourceIdentifier>
          <jpcoar:sourceTitle>情報処理学会論文誌数理モデル化と応用（TOM）</jpcoar:sourceTitle>
          <jpcoar:volume>46</jpcoar:volume>
          <jpcoar:issue>SIG2(TOM11)</jpcoar:issue>
          <jpcoar:pageStart>76</jpcoar:pageStart>
          <jpcoar:pageEnd>84</jpcoar:pageEnd>
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            <jpcoar:URI>https://ipsj.ixsq.nii.ac.jp/record/17220/files/IPSJ-TOM4602009.pdf</jpcoar:URI>
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            <datacite:date dateType="Available">2007-01-15</datacite:date>
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