{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T21:17:54Z","timestamp":1776979074212,"version":"3.51.4"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T00:00:00Z","timestamp":1773446400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004421","name":"Warsaw University of Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004421","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.neucom.2026.133371","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T15:58:44Z","timestamp":1773676724000},"page":"133371","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Neural affine multi-models: Low-complexity training, structure selection and predictive control"],"prefix":"10.1016","volume":"681","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6846-2004","authenticated-orcid":false,"given":"Maciej","family":"\u0141awry\u0144czuk","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133371_bib0005","series-title":"Advanced Control of Industrial Processes, Structures and Algorithms","author":"Tatjewski","year":"2007"},{"key":"10.1016\/j.neucom.2026.133371_bib0010","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1007\/s00170-021-07682-3","article-title":"Review on model predictive control: an engineering perspective","volume":"117","author":"Schwenzer","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.neucom.2026.133371_bib0015","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.cherd.2025.04.035","article-title":"Physics-based model as a versatile tool towards advanced process control of the naphtha distillation unit","volume":"218","author":"Stabrov","year":"2025","journal-title":"Chem. Eng. Res. Des."},{"key":"10.1016\/j.neucom.2026.133371_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.jprocont.2023.103158","article-title":"Efficient model predictive control of boiler coal combustion based on NARX neutral network","volume":"134","author":"Hu","year":"2024","journal-title":"J. Process Control"},{"key":"10.1016\/j.neucom.2026.133371_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2025.109009","article-title":"Control of wastewater treatment plants using economic-oriented MPC and attention-based RNN disturbance prediction models","volume":"196","author":"Protoulis","year":"2025","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.neucom.2026.133371_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.conengprac.2020.104424","article-title":"Development of the predictive based control of an autonomous engine cooling system for variable engine operating conditions in SI engines: design, modeling and real-time application","volume":"100","author":"Kaleli","year":"2020","journal-title":"Control Eng. Pract."},{"key":"10.1016\/j.neucom.2026.133371_bib0035","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2020.12.036","article-title":"PWM-driven model predictive speed control for an unmanned surface vehicle with unknown propeller dynamics based on parameter identification and neural prediction","volume":"432","author":"Peng","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133371_bib0040","series-title":"2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems (SCIS-ISIS), Hachijo Island, Japan","article-title":"Inverse model optimization by differential evolution to improve neural predictive control","author":"Morales-Perez","year":"2020"},{"key":"10.1016\/j.neucom.2026.133371_bib0045","series-title":"Studies in Systems, Decision and Control","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-04229-9","article-title":"Computationally efficient model predictive control algorithms: a neural network approach","volume":"vol. 3","author":"\u0141awry\u0144czuk","year":"2014"},{"key":"10.1016\/j.neucom.2026.133371_bib0050","doi-asserted-by":"crossref","first-page":"59","DOI":"10.34768\/amcs-2021-0005","article-title":"A numerically efficient fuzzy MPC algorithm with fast generation of the control signal","volume":"31","author":"Marusak","year":"2021","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"10.1016\/j.neucom.2026.133371_bib0055","doi-asserted-by":"crossref","first-page":"146745","DOI":"10.1109\/ACCESS.2025.3600308","article-title":"Practical nonlinear model predictive control with Kolmogorov-Arnold network models","volume":"13","author":"Schwedersky","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.133371_bib0060","series-title":"Studies in Systems, Decision and Control","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-83815-7","article-title":"Nonlinear predictive control using wiener models: computationally efficient approaches for polynomial and neural structures","volume":"vol. 389","author":"\u0141awry\u0144czuk","year":"2022"},{"key":"10.1016\/j.neucom.2026.133371_bib0065","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.ins.2022.10.078","article-title":"Advanced predictive control for GRU and LSTM networks","volume":"616","author":"Zarzycki","year":"2022","journal-title":"Inf. Sci."},{"key":"10.1016\/j.neucom.2026.133371_bib0070","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.automatica.2018.03.046","article-title":"Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control","volume":"93","author":"Korda","year":"2018","journal-title":"Automatica"},{"key":"10.1016\/j.neucom.2026.133371_bib0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130197","article-title":"Auto-tuning strategy for deep Koopman robust model predictive control design based on advanced metaheuristics","volume":"638","author":"Meng","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133371_bib0080","doi-asserted-by":"crossref","first-page":"9955","DOI":"10.1007\/s11071-024-09615-7","article-title":"Koopman operator-based multi-model for predictive control","volume":"112","author":"\u0141awry\u0144czuk","year":"2024","journal-title":"Nonlinear Dyn."},{"key":"10.1016\/j.neucom.2026.133371_bib0085","doi-asserted-by":"crossref","first-page":"143","DOI":"10.3390\/a13060143","article-title":"Numerically efficient fuzzy MPC algorithm with advanced generation of prediction\u2014application to a chemical reactor","volume":"13","author":"Marusak","year":"2020","journal-title":"Algorithms"},{"key":"10.1016\/j.neucom.2026.133371_bib0090","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3390\/a14010025","article-title":"Advanced construction of the dynamic matrix in numerically efficient fuzzy MPC algorithms","volume":"14","author":"Marusak","year":"2021","journal-title":"Algorithms"},{"key":"10.1016\/j.neucom.2026.133371_bib0095","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3390\/a18020079","article-title":"Analytical MPC algorithm using steady-state process model","volume":"18","author":"Marusak","year":"2025","journal-title":"Algorithms"},{"key":"10.1016\/j.neucom.2026.133371_bib0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.129712","article-title":"LSTM and GRU type recurrent neural networks in model predictive control: a review","volume":"632","author":"\u0141awry\u0144czuk","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133371_bib0105","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.isatra.2024.04.019","article-title":"Deep learning based model predictive controller on a magnetic levitation ball system","volume":"149","author":"Peng","year":"2024","journal-title":"ISA Transactions"},{"key":"10.1016\/j.neucom.2026.133371_bib0110","doi-asserted-by":"crossref","first-page":"274","DOI":"10.3390\/act12070274","article-title":"LSTM-CNN network-based state-dependent ARX modeling and predictive control with application to water tank system","volume":"12","author":"Kang","year":"2023","journal-title":"Actuators"},{"key":"10.1016\/j.neucom.2026.133371_bib0115","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.neucom.2022.09.108","article-title":"Input convex neural networks in nonlinear predictive control: a multi-model approach","volume":"513","author":"\u0141awry\u0144czuk","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133371_bib0120","doi-asserted-by":"crossref","first-page":"5233","DOI":"10.1016\/j.ifacol.2020.12.1199","article-title":"Learning affine predictors for MPC of nonlinear systems via artificial neural networks","volume":"53","author":"Masti","year":"2020","journal-title":"IFAC-PapersOnLine"},{"key":"10.1016\/j.neucom.2026.133371_bib0125","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1080\/002071798221515","article-title":"Predictive control for non-linear systems using neural networks","volume":"71","author":"Liu","year":"1998","journal-title":"Int. J. Control"},{"key":"10.1016\/j.neucom.2026.133371_bib0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.conengprac.2023.105810","article-title":"Model predictive control (MPC) of an artificial pancreas with data-driven learning of multi-step-ahead blood glucose predictors","volume":"144","author":"Aiello","year":"2024","journal-title":"Control Eng. Pract."},{"key":"10.1016\/j.neucom.2026.133371_bib0135","series-title":"Lecture Notes in Control and Information Sciences","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/978-3-031-82681-8_5","article-title":"Deep prediction networks for Data-Driven nonlinear model predictive control","volume":"vol. 496","author":"Lazar","year":"2025"},{"key":"10.1016\/j.neucom.2026.133371_bib0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2025.117319","article-title":"Optimization of power distribution in electric vehicle hybrid energy storage system based on RBF neural network and dynamic programming","volume":"130","author":"Wang","year":"2025","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.neucom.2026.133371_bib0145","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.isatra.2017.09.016","article-title":"Model predictive control for systems with fast dynamics using inverse neural models","volume":"72","author":"Stogiannos","year":"2018","journal-title":"ISA Transactions"},{"key":"10.1016\/j.neucom.2026.133371_bib0150","series-title":"2019 IEEE Conference on Control Technology and Applications (CCTA), Hong Kong, China","first-page":"368","article-title":"Model predictive control using linearized radial basis function neural models for water distribution networks","author":"Balla","year":"2019"},{"key":"10.1016\/j.neucom.2026.133371_bib0155","series-title":"2019 Chinese Control Conference (CCC), Guangzhou, China","first-page":"2948","article-title":"RBF neural network based model predictive control algorithm and its application to a CSTR process","author":"Li","year":"2019"},{"key":"10.1016\/j.neucom.2026.133371_bib0160","doi-asserted-by":"crossref","first-page":"253","DOI":"10.3390\/aerospace10030253","article-title":"Radial basis function model-based adaptive model predictive control for trajectory tracking of a clapping-wing micro AIR vehicle","volume":"10","author":"Zhang","year":"2023","journal-title":"Aerospace"},{"key":"10.1016\/j.neucom.2026.133371_bib0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2023.114081","article-title":"RBF-ARX model-based MPC approach to inverted pendulum: an event-triggered mechanism","volume":"176","author":"Tian","year":"2023","journal-title":"Chaos Solitons Fractals"},{"key":"10.1016\/j.neucom.2026.133371_bib0170","doi-asserted-by":"crossref","DOI":"10.1049\/cth2.70045","article-title":"Robust output feedback MPC of antagonistic pneumatic artificial muscle system","volume":"19","author":"Yan","year":"2025","journal-title":"IET Control Theory Appl."},{"key":"10.1016\/j.neucom.2026.133371_bib0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103391","article-title":"Design and development of a new autonomous transportation robot for finished vehicles docking transportation in RO\/RO logistics terminal","volume":"66","author":"Xu","year":"2025","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.neucom.2026.133371_bib0180","doi-asserted-by":"crossref","first-page":"11026","DOI":"10.1016\/j.ifacol.2023.10.803","article-title":"Stochastic MPC for energy hubs using data driven demand forecasting","volume":"56","author":"Micheli","year":"2023","journal-title":"IFAC-PapersOnLine"},{"key":"10.1016\/j.neucom.2026.133371_bib0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.112683","article-title":"Design of a nonlinear dynamic output feedback controller based on a fixed-time RBF disturbance observer for a pemfc AIR supply system","volume":"211","author":"Hu","year":"2023","journal-title":"Measurement"},{"key":"10.1016\/j.neucom.2026.133371_bib0190","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1016\/j.neucom.2009.12.015","article-title":"Training of neural models for predictive control","volume":"73","author":"\u0141awry\u0144czuk","year":"2010","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133371_bib0195","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1016\/j.ifacol.2023.10.1857","article-title":"Fast training of neural affine models for model predictive control: an explicit solution","volume":"56","author":"\u0141awry\u0144czuk","year":"2023","journal-title":"IFAC-PapersOnLine"},{"key":"10.1016\/j.neucom.2026.133371_bib0200","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1109\/TSMCB.2002.1018769","article-title":"Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks","volume":"32","author":"Patra","year":"2002","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"10.1016\/j.neucom.2026.133371_bib0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2025.117380","article-title":"Optimized neural network for soil moisture prediction in precision agriculture","volume":"252","author":"Soni","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.neucom.2026.133371_bib0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.gsf.2021.101313","article-title":"Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays","volume":"13","author":"Xie","year":"2022","journal-title":"Geosci. Front."},{"key":"10.1016\/j.neucom.2026.133371_bib0215","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.isatra.2021.08.008","article-title":"Linear and non-linear dynamics of the epidemics: system identification based parametric prediction models for the pandemic outbreaks","volume":"124","author":"Tutsoy","year":"2022","journal-title":"ISA Transactions"},{"key":"10.1016\/j.neucom.2026.133371_bib0220","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.isatra.2018.03.002","article-title":"Design of a completely model free adaptive control in the presence of parametric, non-parametric uncertainties and random control signal delay","volume":"76","author":"Tutsoy","year":"2018","journal-title":"ISA Transactions"},{"key":"10.1016\/j.neucom.2026.133371_bib0225","doi-asserted-by":"crossref","first-page":"6563","DOI":"10.1016\/j.ifacol.2020.12.073","article-title":"HPIPM: a high-performance quadratic programming framework for model predictive control","volume":"53","author":"Frison","year":"2020","journal-title":"IFAC-PapersOnLine"},{"key":"10.1016\/j.neucom.2026.133371_bib0230","series-title":"Nonlinear Model Predictive Control: Theory and Algorithms","author":"Gr\u00fcne","year":"2007"},{"key":"10.1016\/j.neucom.2026.133371_bib0235","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.ifacol.2017.08.071","article-title":"Three benchmarks addressing open challenges in nonlinear system identification","volume":"50","author":"Schoukens","year":"2017","journal-title":"IFAC-PapersOnLine"},{"key":"10.1016\/j.neucom.2026.133371_bib0240","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.ejcon.2021.01.008","article-title":"Continuous-time system identification with neural networks: model structures and fitting criteria","volume":"59","author":"Forgione","year":"2021","journal-title":"Eur. J. Control"},{"key":"10.1016\/j.neucom.2026.133371_bib0245","doi-asserted-by":"crossref","first-page":"90665","DOI":"10.1109\/ACCESS.2020.2994089","article-title":"Approximate GP inference for nonlinear dynamical system identification using data-driven basis set","volume":"8","author":"Shoukat","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.133371_bib0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejcon.2024.101048","article-title":"Reinforcement learning based MPC with neural dynamical models","volume":"80","author":"Adhau","year":"2024","journal-title":"Eur. J. Control"},{"key":"10.1016\/j.neucom.2026.133371_bib0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106226","article-title":"Model predictive control when utilizing LSTM as dynamic models","volume":"123","author":"Jung","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.133371_bib0260","doi-asserted-by":"crossref","first-page":"24787","DOI":"10.1109\/ACCESS.2021.3057674","article-title":"Impact of MPC embedded performance index on control quality","volume":"9","author":"Doma\u0144ski","year":"2021","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.neucom.2026.133371_bib0265","doi-asserted-by":"crossref","first-page":"10","DOI":"10.3390\/a14010010","article-title":"Tuning of multivariable model predictive control for industrial tasks","volume":"14","author":"Nebeluk","year":"2021","journal-title":"Algorithms"},{"key":"10.1016\/j.neucom.2026.133371_bib0270","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1016\/S0967-0661(98)00036-7","article-title":"Experimental physical parameter estimation of a thyristor driven DC-motor using the HMF-method","volume":"6","author":"Daniel-Berhe","year":"1998","journal-title":"Control Eng. Pract."},{"key":"10.1016\/j.neucom.2026.133371_bib0275","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.ifacol.2020.12.217","article-title":"On Gaussian process based Koopman operators","volume":"53","author":"Lian","year":"2020","journal-title":"IFAC-PapersOnLine"},{"key":"10.1016\/j.neucom.2026.133371_bib0280","doi-asserted-by":"crossref","DOI":"10.1016\/j.automatica.2022.110365","article-title":"Expectation-maximization algorithm for bilinear systems by using the Rauch-Tung-Striebel smoother","volume":"142","author":"Liu","year":"2022","journal-title":"Automatica"},{"key":"10.1016\/j.neucom.2026.133371_bib0285","doi-asserted-by":"crossref","first-page":"9465","DOI":"10.1109\/TII.2024.3384621","article-title":"Compensator-Based Self-Learning: optimal operational control for Two-Time-Scale systems with input constraints","volume":"20","author":"Li","year":"2024","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.neucom.2026.133371_bib0290","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2024.108583","article-title":"Two-dimensional model-free q-learning-based output feedback fault-tolerant control for batch processes","volume":"182","author":"Shi","year":"2024","journal-title":"Comput. Chem. Eng."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092523122600768X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092523122600768X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:29:00Z","timestamp":1776976140000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S092523122600768X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":58,"alternative-id":["S092523122600768X"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133371","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Neural affine multi-models: Low-complexity training, structure selection and predictive control","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133371","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"133371"}}