乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T16:26:54Z","timestamp":1726849614965},"reference-count":29,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T00:00:00Z","timestamp":1538352000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T00:00:00Z","timestamp":1538352000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T00:00:00Z","timestamp":1538352000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T00:00:00Z","timestamp":1538352000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T00:00:00Z","timestamp":1538352000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T00:00:00Z","timestamp":1538352000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T00:00:00Z","timestamp":1538352000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"name":"MINECO","award":["TIN2016-78799-P"]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2018,10]]},"DOI":"10.1016\/j.asoc.2018.07.009","type":"journal-article","created":{"date-parts":[[2018,7,11]],"date-time":"2018-07-11T00:58:36Z","timestamp":1531270716000},"page":"826-834","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"special_numbering":"C","title":["Multi-objective optimal design of submerged arches using extreme learning machine and evolutionary algorithms"],"prefix":"10.1016","volume":"71","author":[{"given":"Alejandro M.","family":"Hern\u00e1ndez-D\u00edaz","sequence":"first","affiliation":[]},{"given":"Andr\u00e9s","family":"Bueno-Crespo","sequence":"additional","affiliation":[]},{"given":"Jorge","family":"P\u00e9rez-Aracil","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9 M.","family":"Cecilia","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2018.07.009_bib0005","doi-asserted-by":"crossref","DOI":"10.1007\/s00419-014-0825-2","article-title":"In-plane buckling of prismatic funicular arches with shear deformations","volume":"84","author":"Attard","year":"2014","journal-title":"Arch. Appl. Mech."},{"key":"10.1016\/j.asoc.2018.07.009_bib0010","series-title":"Hormig\u00f3n armado y pretensado: concreto reforzado y preesforzado","author":"Hern\u00e1ndez Montes","year":"2014"},{"key":"10.1016\/j.asoc.2018.07.009_bib0015","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/0020-7683(81)90019-6","article-title":"Stability and optimum design of arch-type structures","volume":"17","author":"Tadjbakhsh","year":"1981","journal-title":"Int. J. Solids Struct."},{"key":"10.1016\/j.asoc.2018.07.009_bib0020","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/0020-7403(83)90044-9","article-title":"On plane Prager-structures \u2013 I","volume":"25","author":"Rozvany","year":"1983","journal-title":"Int. J. Mech. Sci."},{"key":"10.1016\/j.asoc.2018.07.009_bib0025","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1016\/j.engstruct.2012.11.037","article-title":"Shape optimization of concrete buried arches","volume":"48","author":"Eli\u00e1\u0161","year":"2013","journal-title":"Eng. Struct."},{"key":"10.1016\/j.asoc.2018.07.009_bib0030","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1061\/(ASCE)0733-9445(2000)126:5(627)","article-title":"Submerged funicular arches","volume":"126","author":"Gavin","year":"2000","journal-title":"J. Struct. Eng."},{"key":"10.1016\/j.asoc.2018.07.009_bib0035","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1061\/(ASCE)0733-9445(2005)131:3(399)","article-title":"Approximate solution for the shape of submerged funicular arches with self-weight","volume":"131","author":"Chai","year":"2005","journal-title":"J. Struct. Eng."},{"key":"10.1016\/j.asoc.2018.07.009_bib0040","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1061\/(ASCE)0733-9445(2003)129:8(1087)","article-title":"Geometry of submerged funicular arches in cartesian coordinates","volume":"129","author":"Chai","year":"2003","journal-title":"J. Struct. Eng."},{"key":"10.1016\/j.asoc.2018.07.009_bib0045","first-page":"18","article-title":"A multi-objective evolutionary algorithm using neural networks to approximate fitness evaluations","volume":"6","author":"Gaspar-Cunha","year":"2005","journal-title":"Int. J. Comput. Syst. Signal"},{"key":"10.1016\/j.asoc.2018.07.009_bib0050","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/s10100-013-0289-4","article-title":"A genetic algorithm with neural network fitness function evaluation for imrt beam angle optimization","volume":"22","author":"Dias","year":"2014","journal-title":"Cent. Eur. J. Oper. Res."},{"key":"10.1016\/j.asoc.2018.07.009_bib0055","series-title":"Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences","first-page":"59","article-title":"The role of artificial neural networks in evolutionary optimisation: a review","author":"Maarouf","year":"2015"},{"key":"10.1016\/j.asoc.2018.07.009_bib0060","series-title":"Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations, vol. 61","author":"Ascher","year":"1998"},{"key":"10.1016\/j.asoc.2018.07.009_bib0065","series-title":"Analysis of Numerical Methods","author":"Isaacson","year":"1994"},{"key":"10.1016\/j.asoc.2018.07.009_bib0070","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1061\/(ASCE)0733-9445(2002)128:2(266)","article-title":"Funicular shapes for submerged arches","volume":"128","author":"Wang","year":"2002","journal-title":"J. Struct. Eng."},{"key":"10.1016\/j.asoc.2018.07.009_bib0075","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1061\/(ASCE)0733-9399(2003)129:1(120)","article-title":"Shapes of submerged funicular arches","volume":"129","author":"Fung","year":"2003","journal-title":"J. Eng. Mech."},{"key":"10.1016\/j.asoc.2018.07.009_bib0080","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1081\/SME-120020290","article-title":"Optimization of submerged funicular arches","volume":"31","author":"Wang","year":"2003","journal-title":"Mech. Based Des. Struct. Mach."},{"key":"10.1016\/j.asoc.2018.07.009_bib0085","series-title":"Numerical Optimization: Theoretical and Practical Aspects","author":"Bonnans","year":"2006"},{"key":"10.1016\/j.asoc.2018.07.009_bib0090","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2018.07.009_bib0095","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/0925-2312(94)90053-1","article-title":"Learning and generalization characteristics of the random vector functional-link net","volume":"6","author":"Pao","year":"1994","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2018.07.009_bib0100","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1109\/72.471375","article-title":"Stochastic choice of basis functions in adaptive function approximation and the functional-link net","volume":"6","author":"Igelnik","year":"1995","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.asoc.2018.07.009_bib0105","doi-asserted-by":"crossref","first-page":"3056","DOI":"10.1016\/j.neucom.2007.02.009","article-title":"Convex incremental extreme learning machine","volume":"70","author":"Huang","year":"2007","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2018.07.009_bib0110","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","article-title":"Extreme learning machines: a survey","volume":"2","author":"Huang","year":"2011","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.asoc.2018.07.009_bib0115","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.neucom.2008.01.005","article-title":"A fast pruned-extreme learning machine for classification problem","volume":"72","author":"Rong","year":"2008","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2018.07.009_bib0120","first-page":"247","article-title":"A methodology for building regression models using extreme learning machine: OP-ELM","author":"Miche","year":"2008","journal-title":"ESANN"},{"key":"10.1016\/j.asoc.2018.07.009_bib0125","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/978-3-540-87536-9_16","article-title":"OP-ELM: theory, experiments and a toolbox","author":"Miche","year":"2008","journal-title":"Artificial Neural Networks \u2013 ICANN 2008"},{"key":"10.1016\/j.asoc.2018.07.009_bib0130","series-title":"Proceedings of the European Symposium on Time Series Prediction","first-page":"57","article-title":"A variable selection approach based on the delta test for extreme learning machine models","author":"Mateo","year":"2008"},{"key":"10.1016\/j.asoc.2018.07.009_bib0135","first-page":"177","article-title":"A faster model selection criterion for OP-ELM and OP-KNN: Hannan\u2013Quinn criterion","author":"Miche","year":"2009","journal-title":"ESANN, vol. 9"},{"key":"10.1016\/j.asoc.2018.07.009_bib0140","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1109\/TNN.2009.2036259","article-title":"OP-ELM: optimally pruned extreme learning machine","volume":"21","author":"Miche","year":"2010","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.asoc.2018.07.009_bib0145","first-page":"748","article-title":"Multiresponse sparse regression with application to multidimensional scaling","author":"Simil\u00e4","year":"2005","journal-title":"Artificial Neural Networks: Formal Models and Their Applications \u2013 ICANN 2005"}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494618303958?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494618303958?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T03:08:15Z","timestamp":1716606495000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494618303958"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10]]},"references-count":29,"alternative-id":["S1568494618303958"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2018.07.009","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2018,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-objective optimal design of submerged arches using extreme learning machine and evolutionary algorithms","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2018.07.009","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2018 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}