{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T11:50:31Z","timestamp":1777463431775,"version":"3.51.4"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2016,2,18]],"date-time":"2016-02-18T00:00:00Z","timestamp":1455753600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2017,8]]},"DOI":"10.1007\/s00500-016-2074-5","type":"journal-article","created":{"date-parts":[[2016,2,18]],"date-time":"2016-02-18T18:17:07Z","timestamp":1455819427000},"page":"4635-4659","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A modular ridge randomized neural network with differential evolutionary distributor applied to the estimation of sea ice thickness"],"prefix":"10.1007","volume":"21","author":[{"given":"Ahmad","family":"Mozaffari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. Andrea","family":"Scott","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shojaeddin","family":"Chenouri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nasser L.","family":"Azad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,2,18]]},"reference":[{"issue":"4","key":"2074_CR1","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1175\/2007JCLI1787.1","volume":"21","author":"GI Belchansky","year":"2008","unstructured":"Belchansky GI, Douglas DC, Platonov NG (2008) Fluctuating Arctic sea ice thickness changes estimated by an in situ learned and empirically forced neural network model. J Clim 21(4):716\u2013729","journal-title":"J Clim"},{"key":"2074_CR2","first-page":"321","volume":"2","author":"DS Broomhead","year":"1988","unstructured":"Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321\u2013355","journal-title":"Complex Syst"},{"issue":"1","key":"2074_CR3","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/S0893-6080(02)00167-3","volume":"16","author":"M Burger","year":"2003","unstructured":"Burger M, Neubauer A (2003) Analysis of Tikhonov regularization for function approximation by neural networks. Neural Netw 16(1):79\u201390","journal-title":"Neural Netw"},{"issue":"12","key":"2074_CR4","doi-asserted-by":"crossref","first-page":"14555","DOI":"10.1016\/j.eswa.2011.05.027","volume":"38","author":"L-Y Chuang","year":"2011","unstructured":"Chuang L-Y, Hsiao C-J, Yang C-H (2011) Chaotic particle swarm optimization for data clustering. Expert Syst Appl 38(12):14555\u201314563","journal-title":"Expert Syst Appl"},{"issue":"11","key":"2074_CR5","first-page":"3","volume":"125","author":"Y Ding","year":"2014","unstructured":"Ding Y, Feng Q, Wang T, Fu X (2014) A modular nuerla network architecture with concept. Neurocomputing 125(11):3\u20136","journal-title":"Neurocomputing"},{"key":"2074_CR6","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1038\/497431a","volume":"497","author":"H Eicken","year":"2013","unstructured":"Eicken H (2013) Arctic sea ice needs better forecasts. Nature 497:431\u2013433","journal-title":"Nature"},{"key":"2074_CR7","unstructured":"Farooq A (2000) Biologically inspired modular neural networks. Ph.D. thesis, Virginia Tech"},{"key":"2074_CR8","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4737.001.0001","volume-title":"Modularity of mind: an essay on faculty psychology","author":"JA Fodor","year":"1983","unstructured":"Fodor JA (1983) Modularity of mind: an essay on faculty psychology. MIT Press, Cambridge"},{"issue":"5","key":"2074_CR9","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/TGRS.2004.825587","volume":"42","author":"DK Hall","year":"2004","unstructured":"Hall DK, Key JR, Casey KA, Riggs GA, Cavalieri DJ (2004) Sea ice surface temperature product from MODIS. IEEE Trans Geosci Remote Sens 42(5):1076\u20131087","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2074_CR10","volume-title":"MODIS\/terra sea ice extent 5-min L2 swath 1 km V005","author":"DK Hall","year":"2007","unstructured":"Hall DK, Riggs GA, Salomonson VV (2007) MODIS\/terra sea ice extent 5-min L2 swath 1 km V005. National Snow and Ice Data Center, Boulder"},{"key":"2074_CR11","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning. Number 1","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Number 1. Springer, Berlin"},{"issue":"1","key":"2074_CR12","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/36.368223","volume":"33","author":"D Haverkamp","year":"1995","unstructured":"Haverkamp D, Soh L-K, Tsatsoulis C (1995) A comprehensive, automated approach to determining sea ice thickness from SAR data. IEEE Trans Geosci Remote Sens 33(1):46\u201357","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"2074_CR13","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/0893-6080(91)90009-T","volume":"4","author":"K Hornik","year":"1991","unstructured":"Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4(2):251\u2013257","journal-title":"Neural Netw"},{"issue":"1","key":"2074_CR14","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489\u2013501","journal-title":"Neurocomputing"},{"key":"2074_CR15","doi-asserted-by":"crossref","unstructured":"Huang P-S, Deng L, Hasegawa-Johnson M, He X (2013) Random features for kernel deep convex network. In: 2013 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 3143\u20133147","DOI":"10.1109\/ICASSP.2013.6638237"},{"issue":"2","key":"2074_CR16","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/01431161.2012.712229","volume":"34","author":"K Iwamoto","year":"2013","unstructured":"Iwamoto K, Ohshima KI, Tamura T, Nihashi S (2013) Estimation of thin ice thickness from AMSR-E data in the Chukchi sea. Int J Remote Sens 34(2):468\u2013489","journal-title":"Int J Remote Sens"},{"key":"2074_CR17","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.bspc.2012.10.005","volume":"8","author":"M Javadi","year":"2013","unstructured":"Javadi M, Abbaszadeh ASAA, Sajedin A, Ebrahimpour R (2013) Classification of ECG arrhythmia by a modular neural network based on mixture of experts and negative correlated learning. Biomed Signal Process Control 8:289\u2013296","journal-title":"Biomed Signal Process Control"},{"key":"2074_CR18","doi-asserted-by":"publisher","unstructured":"Kaleschke L, Tian-Kunze X, Maab N, Makynen M, Matthias D (2012) Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period. J Geophys Res 39. doi: 10.1029\/2012GL050916","DOI":"10.1029\/2012GL050916"},{"issue":"1","key":"2074_CR19","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.asoc.2009.12.025","volume":"11","author":"D Karaboga","year":"2011","unstructured":"Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11(1):652\u2013657","journal-title":"Appl Soft Comput"},{"key":"2074_CR20","unstructured":"Le Q, Sarl\u00f3s T, Smola A (2013) Fastfood-approximating kernel expansions in loglinear time. In: Proceedings of the international conference on machine learning"},{"key":"2074_CR21","doi-asserted-by":"crossref","unstructured":"Lin H, Yang L (2012) A hybrid neural network model for sea ice thickness forecasting. In: 2012 eighth international conference on natural computation (ICNC). IEEE, pp 358\u2013361","DOI":"10.1109\/ICNC.2012.6234704"},{"key":"2074_CR22","unstructured":"Lowe D (1989) Adaptive radial basis function nonlinearities, and the problem of generalisation. In: First IEE international conference on (Conf. Publ. No. 313) Artificial neural networks. IET, pp 171\u2013175"},{"issue":"3","key":"2074_CR23","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.cosrev.2009.03.005","volume":"3","author":"M Luko\u0161Evi\u010dIus","year":"2009","unstructured":"Luko\u0161Evi\u010dIus M, Jaeger H (2009) Reservoir computing approaches to recurrent neural network training. Comput Sci Rev 3(3):127\u2013149","journal-title":"Comput Sci Rev"},{"issue":"3","key":"2074_CR24","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1109\/TIE.2003.812470","volume":"50","author":"MRG Meireles","year":"2003","unstructured":"Meireles MRG, Almeida PEM, Simoes MG (2003) A comprehensive review for industrial applicability of artificial neural networks. IEEE Trans Ind Electron 50(3):585\u2013601","journal-title":"IEEE Trans Ind Electron"},{"key":"2074_CR25","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-24139-0","volume-title":"Modular neural networks and type-2 fuzzy systems for pattern recognition. Studies in computational intelligence","author":"P Melin","year":"2012","unstructured":"Melin P (2012) Modular neural networks and type-2 fuzzy systems for pattern recognition. Studies in computational intelligence. Springer, Berlin, Heidelberg"},{"issue":"5","key":"2074_CR26","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1504\/IJBIC.2012.049901","volume":"4","author":"A Mozaffari","year":"2012","unstructured":"Mozaffari A, Fathi A (2012) Identifying the behaviour of laser solid freeform fabrication system using aggregated neural network and the great salmon run optimization algorithm. Int J Bio-Inspir Comput 4(5):330\u2013343","journal-title":"Int J Bio-Inspir Comput"},{"key":"2074_CR27","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.asoc.2013.09.023","volume":"14","author":"A Mozaffari","year":"2014","unstructured":"Mozaffari A, Behzadipour S, Kohani M (2014) Identifying the tool-tissue force in robotic laparoscopic surgery using neuro-evolutionary fuzzy systems and a synchronous self-learning hyper level supervisor. Appl Soft Comput 14:12\u201330","journal-title":"Appl Soft Comput"},{"key":"2074_CR28","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s12293-009-0008-9","volume":"1","author":"F Neri","year":"2009","unstructured":"Neri F, Tirronen V (2009) Scale factor local search in differential evolution. Memet Comput 1:153\u2013171","journal-title":"Memet Comput"},{"key":"2074_CR29","doi-asserted-by":"crossref","unstructured":"Nihashi S, Ohshima KI, Tamura T, Fukamachi Y, Saitoh S (2009) Thickness and production of sea ice in the okhotsk sea coastal polynyas from AMSR-E. J Geophys Res: Oceans 114(C10)","DOI":"10.1029\/2008JC005222"},{"issue":"2","key":"2074_CR30","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/0925-2312(94)90053-1","volume":"6","author":"YH Pao","year":"1994","unstructured":"Pao YH, Park GH, Sobajic DJ (1994) Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6(2):163\u2013180","journal-title":"Neurocomputing"},{"issue":"2","key":"2074_CR31","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1162\/neco.1991.3.2.246","volume":"3","author":"J Park","year":"1991","unstructured":"Park J, Sandberg IW (1991) Universal approximation using radial-basis-function networks. Neural Comput 3(2):246\u2013257","journal-title":"Neural Comput"},{"key":"2074_CR32","doi-asserted-by":"crossref","unstructured":"Pratama M, Anavatti SG, Angelov PP, Lughofer E (2014a) PANFIS: a novel incremental learning machine. Neural Netw Learn Syst IEEE Trans 25(1):55\u201368","DOI":"10.1109\/TNNLS.2013.2271933"},{"key":"2074_CR33","doi-asserted-by":"crossref","unstructured":"Pratama M, Anavatti SG, Lughofer E (2014b) GENEFIS: toward an effective localist network. Fuzzy Syst IEEE Trans 22(3):547\u2013562","DOI":"10.1109\/TFUZZ.2013.2264938"},{"key":"2074_CR34","doi-asserted-by":"crossref","unstructured":"Pratama M, Anavatti S, Lu J (2015a) Recurrent classifier based on an incremental meta-cognitive-based scaffolding algorithm. Fuzzy Syst IEEE Trans 23(6):2048\u20132066","DOI":"10.1109\/TFUZZ.2015.2402683"},{"key":"2074_CR35","doi-asserted-by":"crossref","unstructured":"Pratama M, Anavatti SG, Joo M, Lughofer ED (2015b) pClass: an effective classifier for streaming examples. Fuzzy Syst IEEE Trans 23(2):369\u2013386","DOI":"10.1109\/TFUZZ.2014.2312983"},{"key":"2074_CR36","doi-asserted-by":"publisher","unstructured":"Pratama M, Lu J, Zhang G (2015c) Evolving type-2 fuzzy classifier. Fuzzy Syst IEEE Trans. doi: 10.1109\/TFUZZ.2015.2463732","DOI":"10.1109\/TFUZZ.2015.2463732"},{"key":"2074_CR37","unstructured":"Rahimi A, Recht B (2007) Random features for large-scale kernel machines. In: Advances in neural information processing systems, pp 1177\u20131184"},{"key":"2074_CR38","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.neucom.2013.07.013","volume":"124","author":"MAZ Raja","year":"2014","unstructured":"Raja MAZ, Samar R (2014) Numerical treatment for nonlinear MHD Jeffery\u2013Hamel problem using neural networks optimized with interior point algorithm. Neurocomputing 124:178\u2013194","journal-title":"Neurocomputing"},{"issue":"1","key":"2074_CR39","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/TNN.2010.2089641","volume":"22","author":"Ali Rodan","year":"2011","unstructured":"Rodan Ali, Ti\u0148o Peter (2011) Minimum complexity echo state network. Neural Netw IEEE Trans 22(1):131\u2013144","journal-title":"Neural Netw IEEE Trans"},{"key":"2074_CR40","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-61068-4","volume-title":"Neural networks: a systematic introduction","author":"R Rojas","year":"1996","unstructured":"Rojas R (1996) Neural networks: a systematic introduction. Springer, Berlin"},{"key":"2074_CR41","doi-asserted-by":"crossref","unstructured":"Schmidt WF, Kraaijveld MA, Duin RPW (1992) Feedforward neural networks with random weights. In: Pattern recognition, 1992. Conference B: pattern recognition methodology and systems, Proceedings, 11th IAPR international conference, vol II. IEEE, pp 1\u20134","DOI":"10.1109\/ICPR.1992.201708"},{"issue":"3","key":"2074_CR42","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1175\/MWR-D-11-00014.1","volume":"140","author":"KA Scott","year":"2012","unstructured":"Scott KA, Buehner M, Caya A, Carrieres T (2012) Direct assimilation of AMSR-E brightness temperatures for estimating sea ice concentration. Mon Weather Rev 140(3):997\u20131013","journal-title":"Mon Weather Rev"},{"issue":"5","key":"2074_CR43","doi-asserted-by":"crossref","first-page":"2726","DOI":"10.1109\/TGRS.2013.2265091","volume":"52","author":"KA Scott","year":"2014","unstructured":"Scott KA, Buehner M, Carrieres T (2014) An assessment of sea-ice thickness along the Labrador coast from AMSR-E and MODIS data for operational data assimilation. IEEE Trans Geosci Remote Sens 52(5):2726\u20132737","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"3","key":"2074_CR44","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.swevo.2011.06.003","volume":"1","author":"J Senthilnath","year":"2011","unstructured":"Senthilnath J, Omkar SN, Mani V (2011) Clustering using firefly algorithm: performance study. Swarm Evolut Comput 1(3):164\u2013171","journal-title":"Swarm Evolut Comput"},{"key":"2074_CR45","doi-asserted-by":"crossref","unstructured":"Sheikh RH, Raghuwanshi MM, Jaiswal AN (2008) Genetic algorithm based clustering: a survey. In: International conference on emerging trends in engineering and technology, Nagpur, pp 314\u2013319","DOI":"10.1109\/ICETET.2008.48"},{"issue":"1","key":"2074_CR46","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1109\/TGRS.2003.817819","volume":"42","author":"L-K Soh","year":"2004","unstructured":"Soh L-K, Tsatsoulis C, Gineris D, Bertoia C (2004) ARKTOS: an intelligent system for SAR sea ice image classification. IEEE Trans Geosci Remote Sens 42(1):229\u2013248","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2074_CR47","doi-asserted-by":"crossref","unstructured":"Wang X, Key JR, Liu Y (2010) A thermodynamic model for estimating sea and lake ice thickness with optical satellite data. J Geophys Res: Oceans (1978\u20132012) 115(C12)","DOI":"10.1029\/2009JC005857"},{"key":"2074_CR48","volume-title":"On sea ice","author":"W Weeks","year":"2010","unstructured":"Weeks W (2010) On sea ice. University of Alaska Press, Fairbanks"},{"key":"2074_CR49","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.neunet.2012.09.020","volume":"37","author":"Bernard Widrow","year":"2013","unstructured":"Widrow Bernard, Greenblatt Aaron, Kim Youngsik, Park Dookun (2013) The no-prop algorithm: a new learning algorithm for multilayer neural networks. Neural Netw 37:182\u2013188","journal-title":"Neural Netw"},{"issue":"3","key":"2074_CR50","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1162\/neco.1996.8.3.461","volume":"8","author":"L Wu","year":"1996","unstructured":"Wu L, Moody J (1996) A smoothing regularizer for feedforward and recurrent neural networks. Neural Comput 8(3):461\u2013489","journal-title":"Neural Comput"},{"issue":"3","key":"2074_CR51","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.neunet.2007.04.004","volume":"20","author":"T Yamazaki","year":"2007","unstructured":"Yamazaki T, Tanaka S (2007) The cerebellum as a liquid state machine. Neural Netw 20(3):290\u2013297","journal-title":"Neural Netw"},{"key":"2074_CR52","doi-asserted-by":"crossref","unstructured":"Yu Y, Lindsay RW (2003) Comparison of thin ice thickness distributions derived from RADARSAT Geophysical Processor System and advanced very high resolution radiometer data sets. J Geophys Res: Oceans 108(C12)","DOI":"10.1029\/2002JC001319"},{"key":"2074_CR53","doi-asserted-by":"publisher","unstructured":"Zhang L, Suganthan PN (2015) A comprehensive evaluation of random vector functional link networks. Inf Sci. doi: 10.1016\/j.ins.2015.09.025","DOI":"10.1016\/j.ins.2015.09.025"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-016-2074-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-016-2074-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-016-2074-5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-016-2074-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T18:39:09Z","timestamp":1567622349000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-016-2074-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,2,18]]},"references-count":53,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2017,8]]}},"alternative-id":["2074"],"URL":"https:\/\/doi.org\/10.1007\/s00500-016-2074-5","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,2,18]]}}}