{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T19:57:48Z","timestamp":1760385468415,"version":"3.37.3"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2016,1,30]],"date-time":"2016-01-30T00:00:00Z","timestamp":1454112000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61403331"],"award-info":[{"award-number":["61403331"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2016,12]]},"DOI":"10.1007\/s11063-016-9496-z","type":"journal-article","created":{"date-parts":[[2016,1,30]],"date-time":"2016-01-30T17:50:40Z","timestamp":1454176240000},"page":"813-830","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A Kind of Parameters Self-adjusting Extreme Learning Machine"],"prefix":"10.1007","volume":"44","author":[{"given":"Peifeng","family":"Niu","sequence":"first","affiliation":[]},{"given":"Yunpeng","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Mengning","family":"Li","sequence":"additional","affiliation":[]},{"given":"Shanshan","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Guoqiang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,1,30]]},"reference":[{"key":"9496_CR1","unstructured":"Salakhutdinov R, Larochelle H (2010) Efficient learning of deep Boltzmann machines. In: International conference on artificial intelligence and statistics, pp 693\u2013700"},{"key":"9496_CR2","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-3264-1","volume-title":"The nature of statistical learning theory","author":"V Vapnik","year":"2000","unstructured":"Vapnik V (2000) The nature of statistical learning theory. Springer, Berlin"},{"key":"9496_CR3","unstructured":"Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural network. In: Proceedings of IEEE international joint conference on neural network, vol 2, pp 985\u2013990"},{"key":"9496_CR4","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4615-0377-4","volume-title":"Artificial neural network. Interdisciplinary computing in Java programming","author":"SC Wang","year":"2003","unstructured":"Wang SC (2003) Artificial neural network. Interdisciplinary computing in Java programming. Springer, Heidelberg"},{"issue":"12","key":"9496_CR5","first-page":"3351","volume":"9","author":"L Li","year":"2012","unstructured":"Li L, Liu D, Ouyang J (2012) A new regularization classification method based on extreme learning machine in network data. J Inf Comput Sci 9(12):3351\u20133363","journal-title":"J Inf Comput Sci"},{"issue":"6","key":"9496_CR6","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1007\/s00521-011-0572-z","volume":"21","author":"S Karpagachelvi","year":"2012","unstructured":"Karpagachelvi S, Arthanari M, Sivakumar M (2012) Classification of electrocardiogram signals with support vector machines and extreme learning machine. Neural Comput Appl 21(6):1331\u20131339","journal-title":"Neural Comput Appl"},{"issue":"12","key":"9496_CR7","doi-asserted-by":"crossref","first-page":"2431","DOI":"10.1109\/TCYB.2014.2307862","volume":"44","author":"J Yu","year":"2014","unstructured":"Yu J, Rui Y, Tang YY et al (2014) High-order distance-based multiview stochastic learning in image classification. IEEE Trans Cybern 44(12):2431\u20132442","journal-title":"IEEE Trans Cybern"},{"issue":"11","key":"9496_CR8","doi-asserted-by":"crossref","first-page":"3512","DOI":"10.1016\/j.patcog.2014.05.002","volume":"47","author":"J Yu","year":"2014","unstructured":"Yu J, Hong R, Wang M et al (2014) Image clustering based on sparse patch alignment framework. Pattern Recognit 47(11):3512\u20133519","journal-title":"Pattern Recognit"},{"issue":"16\u201318","key":"9496_CR9","doi-asserted-by":"crossref","first-page":"3028","DOI":"10.1016\/j.neucom.2010.07.012","volume":"73","author":"Y Lan","year":"2010","unstructured":"Lan Y, Soh YC, Huang GB (2010) Two-stage extreme learning machine for regression. Neurocomputing 73(16\u201318):3028\u20133038","journal-title":"Neurocomputing"},{"issue":"13\u201315","key":"9496_CR10","doi-asserted-by":"crossref","first-page":"3066","DOI":"10.1016\/j.neucom.2009.03.016","volume":"72","author":"X Tang","year":"2009","unstructured":"Tang X, Han M (2009) Partial Lanczos extreme learning machine for singal-output regression problems. Neurocomputing 72(13\u201315):3066\u20133076","journal-title":"Neurocomputing"},{"key":"9496_CR11","unstructured":"Mikolov T, Chen K, Corrado G et al (2013) Efficient estimation of word representations in vector space. arXiv preprint. arXiv:1301.3781"},{"issue":"16","key":"9496_CR12","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.1016\/j.neucom.2010.12.041","volume":"74","author":"W Zong","year":"2011","unstructured":"Zong W, Huang GB (2011) Face recognition based on extreme learning machine. Neurocomputing 74(16):2541\u20132551","journal-title":"Neurocomputing"},{"issue":"6","key":"9496_CR13","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1007\/s00521-011-0522-9","volume":"21","author":"C Pan","year":"2012","unstructured":"Pan C, Park DS, Yang Y et al (2012) Leukocyte image segmentation by visual attention and extreme learning machine. Neural Comput Appl 21(6):1217\u20131227","journal-title":"Neural Comput Appl"},{"issue":"11","key":"9496_CR14","doi-asserted-by":"crossref","first-page":"3512","DOI":"10.1016\/j.patcog.2014.05.002","volume":"47","author":"J Yu","year":"2014","unstructured":"Yu J, Hong R, Wang M et al (2014) Image clustering based on sparse patch alignment framework. Pattern Recognit 47(11):3512\u20133519","journal-title":"Pattern Recognit"},{"issue":"5","key":"9496_CR15","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1109\/TIP.2014.2311377","volume":"23","author":"J Yu","year":"2014","unstructured":"Yu J, Rui Y, Tao D (2014) Click prediction for web image reranking using multimodal sparse coding. IEEE Trans Image Process 23(5):2019\u20132032","journal-title":"IEEE Trans Image Process"},{"key":"9496_CR16","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.neucom.2011.12.051","volume":"102","author":"S Wu","year":"2013","unstructured":"Wu S, Wang Y, Cheng S (2013) Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system. Neurocomputing 102:163\u2013175","journal-title":"Neurocomputing"},{"key":"9496_CR17","doi-asserted-by":"crossref","unstructured":"Luo Y, Tang J, Yan J et al (2014) Pre-trained multi-view word embedding using two-side neural network. In: Twenty-eighth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v28i1.8956"},{"key":"9496_CR18","doi-asserted-by":"crossref","DOI":"10.1002\/9781118559963","volume-title":"Modern machine learning techniques and their applications in cartoon animation research","author":"J Yu","year":"2013","unstructured":"Yu J, Tao D (2013) Modern machine learning techniques and their applications in cartoon animation research. Wiley, Hoboken"},{"key":"9496_CR19","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s11063-012-9236-y","volume":"36","author":"JW Cao","year":"2012","unstructured":"Cao JW, Lin ZP, Huang GB (2012) Self-adaptive evolutionary extreme learning machine. Neural Process Lett 36:285\u2013305","journal-title":"Neural Process Lett"},{"key":"9496_CR20","doi-asserted-by":"crossref","first-page":"1759","DOI":"10.1016\/j.patcog.2005.03.028","volume":"38","author":"QY Zhu","year":"2005","unstructured":"Zhu QY, Qin AK, Suganthan PN, Huang GB (2005) Evolutionary extreme learning machine. Pattern Recognit 38:1759\u20131763","journal-title":"Pattern Recognit"},{"key":"9496_CR21","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.neucom.2013.09.016","volume":"129","author":"T Matias","year":"2014","unstructured":"Matias T, Souza F, Araujo R, Antunes CH (2014) Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine. Neurocomputing 129:428\u2013436","journal-title":"Neurocomputing"},{"key":"9496_CR22","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.neucom.2011.12.062","volume":"116","author":"F Han","year":"2013","unstructured":"Han F, Yao HF, Ling QH (2013) An improved evolutionary extreme learning machine based on particle swarm optimization. Neurocomputing 116:87\u201393","journal-title":"Neurocomputing"},{"key":"9496_CR23","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.knosys.2014.04.042","volume":"67","author":"GQ Li","year":"2014","unstructured":"Li GQ, Niu PF, Ma YP, Wang HB, Zhang WP (2014) Tuning extreme learning machine by an improved artificial bee colony to model and optimize the boiler efficiency. Knowl Based Syst 67:278\u2013289","journal-title":"Knowl Based Syst"},{"key":"9496_CR24","doi-asserted-by":"crossref","first-page":"3132","DOI":"10.1016\/j.asoc.2012.06.016","volume":"12","author":"GQ Li","year":"2012","unstructured":"Li GQ, Niu PF, Liu C, Zhang WP (2012) Enhanced combination modeling method for combustion efficiency in coal-fired boilers. Appl Soft Comput 12:3132\u20133140","journal-title":"Appl Soft Comput"},{"key":"9496_CR25","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.chemolab.2013.04.012","volume":"126","author":"GQ Li","year":"2013","unstructured":"Li GQ, Niu PF, Zhang WP, Liu YC (2013) Model NOx emissions by least squares support vector machine with tuning based on ameliorated teaching-learning-based optimization. Chemom Intell Lab Syst 126:11\u201320","journal-title":"Chemom Intell Lab Syst"},{"key":"9496_CR26","doi-asserted-by":"publisher","unstructured":"Li GQ, Niu PF, Duan XL, Zhang XY (2013) Fast learning network: a novel artificial neural network with a fast learning speed. Neural Comput Appl. doi: 10.1007\/s00521-013-1398-7","DOI":"10.1007\/s00521-013-1398-7"},{"key":"9496_CR27","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/TNN.2006.880583","volume":"17","author":"NY Liang","year":"2006","unstructured":"Liang NY, Huang gb, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17:1411\u20131423","journal-title":"IEEE Trans Neural Netw"},{"issue":"3","key":"9496_CR28","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.cad.2010.12.015","volume":"43","author":"RV Rao","year":"2011","unstructured":"Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303\u2013315","journal-title":"Comput Aided Des"},{"key":"9496_CR29","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.engappai.2011.08.005","volume":"25","author":"H Zhou","year":"2012","unstructured":"Zhou H, Zhao JP, Zheng LG, Wang CL, Cen KF (2012) Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization. Eng Appl Artif Intell 25:147\u2013158","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"9496_CR30","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TII.2006.890530","volume":"3","author":"Z Song","year":"2007","unstructured":"Song Z, Kusiak A (2007) Constraint-based control of boiler efficiency: a data-mining approach. IEEE Trans Ind Inform 3(1):73\u201383","journal-title":"IEEE Trans Ind Inform"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-016-9496-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11063-016-9496-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-016-9496-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-016-9496-z","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T17:51:50Z","timestamp":1692208310000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11063-016-9496-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,1,30]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2016,12]]}},"alternative-id":["9496"],"URL":"https:\/\/doi.org\/10.1007\/s11063-016-9496-z","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2016,1,30]]}}}