{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T01:53:53Z","timestamp":1725155633948},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2018,9,1]],"date-time":"2018-09-01T00:00:00Z","timestamp":1535760000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundations of China","doi-asserted-by":"crossref","award":["61503252","61473194"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2016T90799"],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Youth Foundation of Hebei Province Department of Education Fund","award":["QN2016140"]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2018,9]]},"DOI":"10.1016\/j.asoc.2017.08.006","type":"journal-article","created":{"date-parts":[[2017,8,14]],"date-time":"2017-08-14T15:47:26Z","timestamp":1502725646000},"page":"959-979","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":29,"special_numbering":"C","title":["Random weight network-based fuzzy nonlinear regression for trapezoidal fuzzy number data"],"prefix":"10.1016","volume":"70","author":[{"given":"Yu-Lin","family":"He","sequence":"first","affiliation":[]},{"given":"Cheng-Hao","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Long","sequence":"additional","affiliation":[]},{"given":"Rana Aamir","family":"Raza Ashfaq","sequence":"additional","affiliation":[]},{"given":"Joshua Zhexue","family":"Huang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.asoc.2017.08.006_bib0005","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/S0165-0114(97)00375-8","article-title":"Insight of a fuzzy regression model","volume":"112","author":"Wang","year":"2000","journal-title":"Fuzzy Sets Syst."},{"issue":"3","key":"10.1016\/j.asoc.2017.08.006_bib0010","doi-asserted-by":"crossref","first-page":"336","DOI":"10.20965\/jaciii.2011.p0336","article-title":"Fuzzy nonlinear regression analysis using fuzzified neural networks for fault diagnosis of chemical plants","volume":"15","author":"Kimura","year":"2011","journal-title":"J. Adv. Comput. Intell. Intell. Inform."},{"key":"10.1016\/j.asoc.2017.08.006_bib0015","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.asoc.2013.11.019","article-title":"A non-linear fuzzy regression for estimating reliability in a degradation process","volume":"16","author":"Gonzalez-Gonzalez","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2017.08.006_bib0020","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.neucom.2014.01.053","article-title":"Using fuzzy non-linear regression to identify the degree of compensation among customer requirements in QFD","volume":"142","author":"Liu","year":"2014","journal-title":"Neurocomputing"},{"issue":"5","key":"10.1016\/j.asoc.2017.08.006_bib0025","doi-asserted-by":"crossref","first-page":"2329","DOI":"10.3233\/IFS-141516","article-title":"Learning from big data with uncertainty-editorial","volume":"28","author":"Wang","year":"2015","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"3","key":"10.1016\/j.asoc.2017.08.006_bib0030","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/0165-0114(92)90224-R","article-title":"Fuzzy regression analysis using neural networks","volume":"50","author":"Ishibuchi","year":"1992","journal-title":"Fuzzy Sets Syst."},{"key":"10.1016\/j.asoc.2017.08.006_bib0035","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"issue":"1","key":"10.1016\/j.asoc.2017.08.006_bib0040","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/0165-0114(93)90118-2","article-title":"An architecture of neural networks with interval weights and its application to fuzzy regression analysis","volume":"57","author":"Ishibuchi","year":"1993","journal-title":"Fuzzy Sets Syst."},{"issue":"3","key":"10.1016\/j.asoc.2017.08.006_bib0045","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/0165-0114(94)00281-B","article-title":"A learning algorithm of fuzzy neural networks with triangular fuzzy weights","volume":"71","author":"Ishibuchi","year":"1995","journal-title":"Fuzzy Sets Syst."},{"issue":"2","key":"10.1016\/j.asoc.2017.08.006_bib0050","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/S0165-0114(99)00098-6","article-title":"Fuzzy regression with radial basis function network","volume":"119","author":"Cheng","year":"2001","journal-title":"Fuzzy Sets Syst."},{"key":"10.1016\/j.asoc.2017.08.006_bib0055","first-page":"228","article-title":"A fuzzy neural network with trapezoid fuzzy weights","author":"Ishibuchi","year":"1994","journal-title":"Proceedings of the Third IEEE Conference on Fuzzy Systems"},{"key":"10.1016\/j.asoc.2017.08.006_bib0060","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.ins.2016.01.037","article-title":"Fuzzy nonlinear regression analysis using a random weight network","volume":"364\u2013365","author":"He","year":"2016","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2017.08.006_bib0065","first-page":"1","article-title":"Feedforward neural networks with random weights","author":"Schmidt","year":"1992","journal-title":"Proceedings of 11th IAPR International Conference on Pattern Recognition, Conference B: Pattern Recognition Methodology and Systems vol. II"},{"key":"10.1016\/j.asoc.2017.08.006_bib0070","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.knosys.2014.11.014","article-title":"A local learning algorithm for random weights networks","volume":"74","author":"Zhao","year":"2015","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.asoc.2017.08.006_bib0075","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1016\/j.ins.2015.09.002","article-title":"An iterative learning algorithm for feedforward neural networks with random weights","volume":"328","author":"Cao","year":"2016","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2017.08.006_bib0080","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.ins.2015.03.039","article-title":"A probabilistic learning algorithm for robust modeling using neural networks with random weights","volume":"313","author":"Cao","year":"2015","journal-title":"Inf. Sci."},{"issue":"3","key":"10.1016\/j.asoc.2017.08.006_bib0085","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/S0165-0114(98)00037-2","article-title":"Fuzzy economic production for production inventory","volume":"111","author":"Lin","year":"2000","journal-title":"Fuzzy Sets Syst."},{"issue":"2","key":"10.1016\/j.asoc.2017.08.006_bib0090","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.eswa.2005.09.040","article-title":"Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment","volume":"31","author":"Wang","year":"2006","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"10.1016\/j.asoc.2017.08.006_bib0095","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.ssci.2011.08.042","article-title":"Application of a trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments","volume":"50","author":"Zheng","year":"2012","journal-title":"Saf. Sci."},{"key":"10.1016\/j.asoc.2017.08.006_bib0100","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/978-3-642-01020-0_30","article-title":"A trapezoidal fuzzy numbers-based approach for aggregating group preferences and ranking decision alternatives in MCDM","volume":"5467","author":"Mateos","year":"2009","journal-title":"Lecture Notes in Comput. Sci."},{"issue":"6","key":"10.1016\/j.asoc.2017.08.006_bib0105","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."},{"issue":"5","key":"10.1016\/j.asoc.2017.08.006_bib0110","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/2.144401","article-title":"Functional-link net computing: theory system architecture and functionalities","volume":"25","author":"Pao","year":"1992","journal-title":"Computer"},{"key":"10.1016\/j.asoc.2017.08.006_bib0115","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.1016\/j.ins.2015.11.039","article-title":"Random vector functional link network for short-term electricity load demand forecasting","volume":"367","author":"Ren","year":"2016","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2017.08.006_bib0120","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.ins.2016.01.039","article-title":"A survey of randomized algorithms for training neural networks","volume":"364","author":"Zhang","year":"2016","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2017.08.006_bib0125","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1016\/j.ins.2015.09.025","article-title":"A comprehensive evaluation of random vector functional link networks","volume":"367","author":"Zhang","year":"2016","journal-title":"Inf. Sci."},{"issue":"19","key":"10.1016\/j.asoc.2017.08.006_bib0130","doi-asserted-by":"crossref","first-page":"3806","DOI":"10.1016\/j.ins.2010.05.023","article-title":"Some notes on Zadeh's extensions","volume":"180","author":"Huang","year":"2010","journal-title":"Inf. Sci."},{"issue":"13","key":"10.1016\/j.asoc.2017.08.006_bib0135","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1016\/j.ins.2008.02.012","article-title":"Is there a need for fuzzy logic?","volume":"178","author":"Zadeh","year":"2008","journal-title":"Inf. Sci."},{"issue":"1","key":"10.1016\/j.asoc.2017.08.006_bib0140","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"},{"issue":"5","key":"10.1016\/j.asoc.2017.08.006_bib0145","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1109\/TSMCB.2007.901375","article-title":"A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines","volume":"37","author":"Sun","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. Part B: Cybern."},{"issue":"January","key":"10.1016\/j.asoc.2017.08.006_bib0150","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Dem\u0161ar","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.asoc.2017.08.006_bib0155","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.ins.2016.04.019","article-title":"Fuzziness based semi-supervised learning approach for intrusion detection system","volume":"378","author":"Ashfaq","year":"2017","journal-title":"Inf. Sci."},{"issue":"1","key":"10.1016\/j.asoc.2017.08.006_bib0160","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.eswa.2014.07.018","article-title":"OWA operator based link prediction ensemble for social network","volume":"42","author":"He","year":"2015","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"10.1016\/j.asoc.2017.08.006_bib0165","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.3233\/IFS-151729","article-title":"Fuzziness based sample categorization for classifier performance improvement","volume":"29","author":"Wang","year":"2015","journal-title":"J. Intell. Fuzzy Syst."},{"key":"10.1016\/j.asoc.2017.08.006_bib0170","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.neucom.2015.05.069","article-title":"Lightness biased cartoon-and-texture decomposition for textile image segmentation","volume":"168","author":"Han","year":"2015","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2017.08.006_bib0175","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/j.neucom.2015.05.065","article-title":"Study on novel curvature features for 3D fingerprint recognition","volume":"168","author":"Liu","year":"2015","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2017.08.006_bib0180","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.neucom.2015.03.031","article-title":"Manifold discriminant regression learning for image classification","volume":"166","author":"Lu","year":"2015","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2017.08.006_bib0185","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.neucom.2015.06.013","article-title":"Joint representation and pattern learning for robust face recognition","volume":"168","author":"Yang","year":"2015","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2017.08.006_bib0190","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.knosys.2012.08.003","article-title":"Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon","volume":"37","author":"Fu","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.asoc.2017.08.006_bib0195","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.neucom.2015.06.047","article-title":"Dynamic online HDP model for discovering evolutionary topics from Chinese social texts","volume":"171","author":"Fu","year":"2016","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2017.08.006_bib0200","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.knosys.2015.02.021","article-title":"Dynamic non-parametric joint sentiment topic mixture model","volume":"82","author":"Fu","year":"2015","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.asoc.2017.08.006_bib0205","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.neucom.2013.03.073","article-title":"Predicting dynamic deformation of retaining structure by LSSVR-based time series method","volume":"137","author":"Ji","year":"2014","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2017.08.006_bib0210","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.neucom.2011.12.013","article-title":"A novel text mining approach to financial time series forecasting","volume":"83","author":"Wang","year":"2012","journal-title":"Neurocomputing"},{"issue":"3","key":"10.1016\/j.asoc.2017.08.006_bib0215","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/21.256541","article-title":"ANFIS: adaptive-network-based fuzzy inference system","volume":"23","author":"Jang","year":"1993","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"10.1016\/j.asoc.2017.08.006_bib0220","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.ins.2013.03.004","article-title":"Passive and exponential filter design for fuzzy neural networks","volume":"238","author":"Ahn","year":"2013","journal-title":"Inf. Sci."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S156849461730488X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S156849461730488X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T23:52:54Z","timestamp":1606348374000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S156849461730488X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9]]},"references-count":44,"alternative-id":["S156849461730488X"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2017.08.006","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2018,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Random weight network-based fuzzy nonlinear regression for trapezoidal fuzzy number data","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2017.08.006","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2017 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}