{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:06:50Z","timestamp":1761808010032,"version":"3.41.0"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2016,9,1]],"date-time":"2016-09-01T00:00:00Z","timestamp":1472688000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51175079","51305176"],"award-info":[{"award-number":["51175079","51305176"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"\u201cthe Fundamental Research Funds for the Central Universities, SEU\u201d"},{"name":"Jiangsu Province for the College Graduate Research and Innovation Projects","award":["KYLX_0097"],"award-info":[{"award-number":["KYLX_0097"]}]},{"name":"Scientific Research Foundation of Graduate School of Southeast University","award":["3202005717"],"award-info":[{"award-number":["3202005717"]}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2016,9]]},"DOI":"10.1016\/j.neucom.2016.03.075","type":"journal-article","created":{"date-parts":[[2016,5,22]],"date-time":"2016-05-22T09:05:17Z","timestamp":1463907917000},"page":"150-164","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":22,"special_numbering":"C","title":["Improvement on the linear and nonlinear auto-regressive model for predicting the NOx emission of diesel engine"],"prefix":"10.1016","volume":"207","author":[{"given":"Jiaxin","family":"Ma","sequence":"first","affiliation":[]},{"given":"Feiyun","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Ren","family":"Huang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2016.03.075_bib1","doi-asserted-by":"crossref","unstructured":"Q. Song, G. Zhu, Model-based closed-loop control of urea SCR exhaust aftertreatment system for diesel engine, SAE paper 2002-01-0287.","DOI":"10.4271\/2002-01-0287"},{"issue":"18","key":"10.1016\/j.neucom.2016.03.075_bib2","doi-asserted-by":"crossref","first-page":"7842","DOI":"10.1016\/j.ijhydene.2009.07.059","article-title":"Promoting hydrocarbon-SCR of NOx in diesel engine exhaust by hydrogen and fuel reforming","volume":"34","author":"Sitshebo","year":"2009","journal-title":"Int. J. Hydrog. Energy"},{"issue":"12","key":"10.1016\/j.neucom.2016.03.075_bib3","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1002\/rnc.1264","article-title":"Neural networks for real-time nonlinear control of a variable geometry turbocharged diesel engine","volume":"18","author":"Omran","year":"2008","journal-title":"Int. J. Robust Nonlinear Control"},{"issue":"3","key":"10.1016\/j.neucom.2016.03.075_bib4","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1021\/es203079n","article-title":"Influence of real-world engine load conditions on nanoparticle emissions from a DPF and SCR equipped heavy-duty diesel engine","volume":"46","author":"Thiruvengadam","year":"2012","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.neucom.2016.03.075_bib5","doi-asserted-by":"crossref","unstructured":"S. Sasaki, J. Sarlashkar, G.D. Neely, J.M. Wang, Q.L. Lu, H. Sono, Investigation of alternative combustion, airflow dominant control and aftertreatment systems for clean diesel vehicles. SAE Paper 2007-01-1937.","DOI":"10.4271\/2007-01-1937"},{"issue":"2","key":"10.1016\/j.neucom.2016.03.075_bib6","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/0360-1285(91)90019-J","article-title":"Combustion processes in diesel engines","volume":"17","author":"Kamimoto","year":"1991","journal-title":"Prog. Energy Combust. Sci."},{"key":"10.1016\/j.neucom.2016.03.075_bib7","doi-asserted-by":"crossref","unstructured":"U. Gartner, G. Hohenberg, H. Daudel, H. Oelschlegel, Development and application of a semi-empirical NOX model to various HD diesel engines, In: Thiesel 2002 Conference on Thermo \u2013 and Fluid Dynamic Processes in Diesel Engines, Valencia, Spain, Sep. 2002, pp. 285\u2013312.","DOI":"10.1007\/978-3-662-10502-3_14"},{"key":"10.1016\/j.neucom.2016.03.075_bib8","doi-asserted-by":"crossref","unstructured":"J.H. Li, Q. Wang, B.C. Yan, L.S. Guo, Study on Urea-SCR system basing on pre-oxidation, In: Proceedings of the International Conference on Electric Information and Control Engineering, Wuhan, China, Apr. 2011, pp. 5415\u20135418.","DOI":"10.1109\/ICEICE.2011.5776890"},{"issue":"6","key":"10.1016\/j.neucom.2016.03.075_bib9","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1109\/TCST.2011.2169494","article-title":"Adaptive model predictive control of an SCR catalytic converter system for automotive applications","volume":"20","author":"McKinley","year":"2012","journal-title":"IEEE Trans. Control Syst. Technol."},{"issue":"1","key":"10.1016\/j.neucom.2016.03.075_bib10","doi-asserted-by":"crossref","first-page":"646","DOI":"10.4271\/2008-01-1324","article-title":"Model-based estimation and control system development in a urea-SCR aftertreatment system","volume":"1","author":"Devarakonda","year":"2009","journal-title":"SAE Int. J. Fuels Lubr."},{"key":"10.1016\/j.neucom.2016.03.075_bib11","doi-asserted-by":"crossref","unstructured":"J. Arregle, J. Lopez, C. Guardiola, C. Monin, Sensitivity study of a NOx estimation model for on-board applications, SAE Paper 2008-01-0640.","DOI":"10.4271\/2008-01-0640"},{"issue":"2","key":"10.1016\/j.neucom.2016.03.075_bib12","first-page":"1","article-title":"Study of control strategy for urea-SCR after-treatment system of heavy duty diesel engine","volume":"32","author":"Hu","year":"2011","journal-title":"Chin. Intern. Combust. Engine Eng."},{"key":"10.1016\/j.neucom.2016.03.075_bib13","series-title":"Automotive Model Predictive Control: Models, Methods and Applications","first-page":"25","article-title":"On board NOX prediction in diesel engines: a physical approach","author":"Arregle","year":"2010"},{"issue":"9","key":"10.1016\/j.neucom.2016.03.075_bib14","doi-asserted-by":"crossref","first-page":"092806","DOI":"10.1115\/1.4006942","article-title":"Real-time transient soot and NOX virtual sensors for diesel engine using neuro-fuzzy model tree and orthogonal least squares","volume":"134","author":"Johri","year":"2012","journal-title":"J. Eng. Gas Turbines Power"},{"key":"10.1016\/j.neucom.2016.03.075_bib15","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.apenergy.2012.09.038","article-title":"A fast and accurate physics-based model for the NOX emissions of diesel engines","volume":"103","author":"Asprion","year":"2013","journal-title":"Appl. Energy"},{"key":"10.1016\/j.neucom.2016.03.075_bib16","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1016\/j.fuel.2013.01.071","article-title":"A comparison of two NOX prediction schemes for use in diesel engine thermodynamic modeling","volume":"107","author":"Rao","year":"2013","journal-title":"Fuel"},{"issue":"5","key":"10.1016\/j.neucom.2016.03.075_bib17","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1111\/j.1744-7402.2005.02041.x","article-title":"A brief overview on automotive exhaust gas sensors based on electroceramics","volume":"2","author":"Moos","year":"2005","journal-title":"Int. J. Appl. Ceram. Technol."},{"issue":"4","key":"10.1016\/j.neucom.2016.03.075_bib18","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1109\/TCST.2006.876634","article-title":"Control of an SCR catalytic converter system for a mobile heavy-duty application","volume":"14","author":"Schar","year":"2006","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"10.1016\/j.neucom.2016.03.075_bib19","doi-asserted-by":"crossref","unstructured":"F. Willems, R. Cloudt, E. van den Eijnden, M. van Genderen, R. Verbeek, B. de Jager, et al., Is closed-loop SCR control required to meet future emission targets, SAE Technical Paper 2007-01-1574.","DOI":"10.4271\/2007-01-1574"},{"issue":"4","key":"10.1016\/j.neucom.2016.03.075_bib20","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.conengprac.2010.12.002","article-title":"Design and experimental validation of an extended Kalman filter-based NOX concentration estimator in selective catalytic reduction system applications","volume":"19","author":"Hsieh","year":"2011","journal-title":"Control Eng. Pract."},{"issue":"2","key":"10.1016\/j.neucom.2016.03.075_bib21","first-page":"396","article-title":"SparseFIS: data-driven learning of fuzzy systems with sparsity constraints","volume":"18","author":"Lughofer","year":"2010","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2016.03.075_bib22","doi-asserted-by":"crossref","unstructured":"E. Lughofer, V. Macian, C. Guardiola, E.P. Klement, Data-driven design of Takagi-Sugeno fuzzy systems for predicting NOx emissions, In: Proceedings of the 13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Dortmund, Germany, Jun.\u2013Jul. 2010, pp. 1\u201310.","DOI":"10.1007\/978-3-642-14058-7_1"},{"issue":"2","key":"10.1016\/j.neucom.2016.03.075_bib23","doi-asserted-by":"crossref","first-page":"2487","DOI":"10.1016\/j.asoc.2010.10.004","article-title":"Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems","volume":"11","author":"Lughofer","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.neucom.2016.03.075_bib24","unstructured":"A. Stotsky, B. Egardt, Data-driven estimation of the inertia moment of wind turbines: a new ice-detection algorithm, Proc. IMechE Part I: J. Syst. Control Eng. 277(6) (2013) 552\u2013555."},{"key":"10.1016\/j.neucom.2016.03.075_bib25","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.neucom.2012.11.013","article-title":"Data driven modeling based on dynamic parsimonious fuzzy neural network","volume":"100","author":"Pratama","year":"2013","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2016.03.075_bib26","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1016\/j.neucom.2015.04.034","article-title":"A new data-driven neural fuzzy system with collaborative fuzzy clustering mechanism","volume":"167","author":"Prasad","year":"2015","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2016.03.075_bib27","unstructured":"P. Marenbach, K.D. Bettenhausen, S. Freyer, U. Nieken, H. Rettenmaier, Data-driven structured modelling of a biotechnological fed-batch fermentation by means of genetic programming, Proc. IMechE, Part I: J. Syst. Control Eng. 211(5) (2013) 325\u2013332."},{"key":"10.1016\/j.neucom.2016.03.075_bib28","unstructured":"R.D. Burke, W. Baumann, S. Akehurst, C.J. Brace, Dynamic modelling of diesel engine emissions using the parametric Volterra series, Proc. IMechE Part D: J. Automob. Eng. 228(2) (2014) 164\u2013179."},{"issue":"2","key":"10.1016\/j.neucom.2016.03.075_bib29","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/j.ymssp.2005.11.005","article-title":"Application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine: a case study","volume":"21","author":"Antory","year":"2007","journal-title":"Mech. Syst. Sig. Process"},{"issue":"9","key":"10.1016\/j.neucom.2016.03.075_bib30","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1049\/iet-cta.2011.0204","article-title":"Non-iterative direct data-driven controller tuning for multivariable systems: theory and application","volume":"6","author":"Formentin","year":"2012","journal-title":"IET Control Theory Appl."},{"issue":"4","key":"10.1016\/j.neucom.2016.03.075_bib31","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.conengprac.2012.12.006","article-title":"Automotive engine FDI by application of an automated model-based and data-driven design methodology","volume":"21","author":"Svard","year":"2013","journal-title":"Control Eng. Pract."},{"key":"10.1016\/j.neucom.2016.03.075_bib32","first-page":"509","article-title":"A linear and nonlinear auto-regressive model and its application in modeling and forecasting","volume":"43","author":"Ma","year":"2013","journal-title":"J. Southeast Univ.: Nat. Sci. Ed."},{"issue":"1","key":"10.1016\/j.neucom.2016.03.075_bib33","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s11465-009-0015-z","article-title":"General expression for linear and nonlinear time series models","volume":"4","author":"Huang","year":"2009","journal-title":"Front. Mech. Eng. Chin."},{"issue":"7","key":"10.1016\/j.neucom.2016.03.075_bib34","doi-asserted-by":"crossref","first-page":"243","DOI":"10.3901\/JME.2009.07.243","article-title":"Distortion correction method based on mathematic model in machine vision measurement system","volume":"45","author":"Chen","year":"2009","journal-title":"Chin. J. Mech. Eng."},{"year":"1986","series-title":"Introduction to System Identification","author":"Xu","key":"10.1016\/j.neucom.2016.03.075_bib35"},{"key":"10.1016\/j.neucom.2016.03.075_bib36","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.neucom.2012.01.024","article-title":"An evolutionary constructive and pruning algorithm for artificial neural networks and its prediction applications","volume":"86","author":"Yang","year":"2012","journal-title":"Neurocomputing"},{"issue":"4598","key":"10.1016\/j.neucom.2016.03.075_bib37","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.220.4598.671","article-title":"Optimization by simulated annealing","volume":"220","author":"Kirkpatrick","year":"1983","journal-title":"Science"},{"issue":"4","key":"10.1016\/j.neucom.2016.03.075_bib38","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ijforecast.2006.03.001","article-title":"Another look at measures of forecast accuracy","volume":"22","author":"Hyndman","year":"2006","journal-title":"Int. J. Forecast."},{"issue":"2","key":"10.1016\/j.neucom.2016.03.075_bib39","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1093\/biomet\/65.2.297","article-title":"On a measure of lack of \ufb01t in time series models","volume":"65","author":"Ljung","year":"1978","journal-title":"Biometrika"},{"year":"2007","series-title":"Time Series Analysis in Engineering Application","author":"Yang","key":"10.1016\/j.neucom.2016.03.075_bib40"},{"issue":"2","key":"10.1016\/j.neucom.2016.03.075_bib41","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1109\/72.80236","article-title":"A simple procedure for pruning back-propagation trained neural networks","volume":"1","author":"Karnin","year":"1990","journal-title":"IEEE Trans. Neural Netw."},{"issue":"1","key":"10.1016\/j.neucom.2016.03.075_bib42","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1299\/jsmeb.43.89","article-title":"Study on predicting combustion and NOX formation in diesel engines from scale model experiments","volume":"43","author":"Kikuta","year":"2000","journal-title":"JSME Int. J. Ser. B"},{"year":"1992","series-title":"Gray Theory and Method","author":"Yi","key":"10.1016\/j.neucom.2016.03.075_bib43"},{"issue":"11","key":"10.1016\/j.neucom.2016.03.075_bib44","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1002\/er.1625","article-title":"Comparative study of turbocharged diesel engine emission during three different transient cycles","volume":"34","author":"Giakoumis","year":"2010","journal-title":"Int. J. Energy Res."},{"issue":"1","key":"10.1016\/j.neucom.2016.03.075_bib45","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/S0927-5398(96)00011-4","article-title":"An artificial neural network-GARCH model for international stock return volatility","volume":"4","author":"Donaldsona","year":"1997","journal-title":"J. Empirical Financ."},{"key":"10.1016\/j.neucom.2016.03.075_bib46","first-page":"427","article-title":"Distribution of the estimators for autoregressive time series with a unit root","volume":"74","author":"Dickey","year":"1979","journal-title":"J. Am. Stat. Assoc."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0925231216303290?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0925231216303290?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T18:05:54Z","timestamp":1748973954000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231216303290"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9]]},"references-count":46,"alternative-id":["S0925231216303290"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1016\/j.neucom.2016.03.075","relation":{},"ISSN":["0925-2312"],"issn-type":[{"type":"print","value":"0925-2312"}],"subject":[],"published":{"date-parts":[[2016,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Improvement on the linear and nonlinear auto-regressive model for predicting the NOx emission of diesel engine","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1016\/j.neucom.2016.03.075","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2016 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}