{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T18:15:14Z","timestamp":1776190514858,"version":"3.50.1"},"reference-count":105,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","award":["23KJA120003"],"award-info":[{"award-number":["23KJA120003"]}],"id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","award":["TZ202513"],"award-info":[{"award-number":["TZ202513"]}],"id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1016\/j.engappai.2025.113495","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T17:15:14Z","timestamp":1765214114000},"page":"113495","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":8,"special_numbering":"PB","title":["A dynamic forgetting factor-based recursive estimation framework for radial basis function-based Hammerstein battery models"],"prefix":"10.1016","volume":"165","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2360-1856","authenticated-orcid":false,"given":"Huafeng","family":"Xia","sequence":"first","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.engappai.2025.113495_b1","first-page":"437","article-title":"The fault diagnosis of a switch machine based on deep random forest fusion","volume":"15","author":"Cao","year":"2023","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"issue":"3","key":"10.1016\/j.engappai.2025.113495_b2","doi-asserted-by":"crossref","first-page":"802","DOI":"10.23919\/cje.2024.00.200","article-title":"Improved YOLOv8 for high-precision detection of rail surface defects on heavy-haul railways","volume":"34","author":"Cao","year":"2025","journal-title":"Chin. J. Electron."},{"issue":"8","key":"10.1016\/j.engappai.2025.113495_b3","doi-asserted-by":"crossref","first-page":"12074","DOI":"10.1109\/TITS.2021.3109632","article-title":"A sound-based fault diagnosis method for railway point machines based on two-stage feature selection strategy and ensemble classifier","volume":"23","author":"Cao","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.engappai.2025.113495_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127123","article-title":"Bi-level sparsity augmented design method for selection of tractive locations of railway turnout","volume":"275","author":"Cao","year":"2025","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"10.1016\/j.engappai.2025.113495_b5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1049\/cje.2021.00.241","article-title":"Research on virtual coupled train control method based on GPC & VAPF","volume":"31","author":"Cao","year":"2022","journal-title":"Chin. J. Electron."},{"issue":"10","key":"10.1016\/j.engappai.2025.113495_b6","doi-asserted-by":"crossref","first-page":"17666","DOI":"10.1109\/TITS.2022.3155628","article-title":"Trajectory optimization for high-speed trains via a mixed integer linear programming approach","volume":"23","author":"Cao","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.engappai.2025.113495_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2024.132583","article-title":"A novel battery SOC estimation method based on random search optimized LSTM neural networ","volume":"306","author":"Chai","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.engappai.2025.113495_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.automatica.2020.109034","article-title":"Modified Kalman filtering based multi-step-length gradient iterative algorithm for ARX models with random missing outputs","volume":"118","author":"Chen","year":"2020","journal-title":"Automatica"},{"issue":"10","key":"10.1016\/j.engappai.2025.113495_b9","doi-asserted-by":"crossref","first-page":"4385","DOI":"10.1109\/TAC.2019.2955030","article-title":"Interval error correction auxiliary model based gradient iterative algorithms for multirate arx models","volume":"65","author":"Chen","year":"2020","journal-title":"IEEE Transactions on Automatic Control"},{"key":"10.1016\/j.engappai.2025.113495_b10","article-title":"Varying infimum gradient descent algorithm for agent-server systems with uncertain communication network","volume":"70","author":"Chen","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"8","key":"10.1016\/j.engappai.2025.113495_b11","doi-asserted-by":"crossref","first-page":"5185","DOI":"10.1109\/TII.2020.3025581","article-title":"Identification of two-dimensional causal systems with missing output data via expectation\u2013maximization algorithm","volume":"17","author":"Chen","year":"2021","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2025.113495_b12","article-title":"An integrated framework for ARX model identification and its application to lithium-ion battery","volume":"74","author":"Chen","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2025.113495_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2025.105266","article-title":"DSR-Net: Distinct selective rollback queries for road cracks detection with detection transformer","volume":"164","author":"Deng","year":"2025","journal-title":"Digit. Signal Process."},{"issue":"10","key":"10.1016\/j.engappai.2025.113495_b14","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1016\/j.automatica.2004.05.001","article-title":"Combined parameter and output estimation of dual-rate systems using an auxiliary model","volume":"40","author":"Ding","year":"2004","journal-title":"Automatica"},{"issue":"9","key":"10.1016\/j.engappai.2025.113495_b15","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1109\/TAC.2005.854654","article-title":"Parameter estimation of dual-rate stochastic systems by using an output error method","volume":"50","author":"Ding","year":"2005","journal-title":"IEEE Trans. Automat. Control"},{"issue":"9","key":"10.1016\/j.engappai.2025.113495_b16","doi-asserted-by":"crossref","first-page":"1978","DOI":"10.1002\/acs.4036","article-title":"Hierarchical recursive gradient parameter identification for multivariable systems with partially-coupled information vectors","volume":"39","author":"Ding","year":"2025","journal-title":"Internat. J. Adapt. Control Signal Process."},{"key":"10.1016\/j.engappai.2025.113495_b17","article-title":"Hierarchical stochastic gradient and hierarchical multi-innovation stochastic gradient identification for multivarible ARX models","author":"Ding","year":"2025","journal-title":"Internat. J. Adapt. Control Signal Process."},{"key":"10.1016\/j.engappai.2025.113495_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.sysconle.2025.106166","article-title":"Two-stage parameter estimation methods for linear time-invariant continuous-time systems","volume":"204","author":"Ding","year":"2025","journal-title":"Systems Control Lett."},{"key":"10.1016\/j.engappai.2025.113495_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.arcontrol.2024.100942","article-title":"Recursive identification methods for general stochastic systems with colored noises by using the hierarchical identification principle and the filtering identification idea","volume":"57","author":"Ding","year":"2024","journal-title":"Annu. Rev. Control."},{"key":"10.1016\/j.engappai.2025.113495_b20","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1002\/rnc.7014","article-title":"Hierarchical gradient- and least-squares-based iterative estimation algorithms for input-nonlinear output-error systems from measurement information by using the over-parameterization","volume":"34","author":"Ding","year":"2024","journal-title":"Internat. J. Robust Nonlinear Control"},{"key":"10.1016\/j.engappai.2025.113495_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.arcontrol.2025.100993","article-title":"Hierarchical generalized extended parameter identification for multivariable equation-error ARMA-like systems by using the filtering identification idea","volume":"60","author":"Ding","year":"2025","journal-title":"Annu. Rev. Control."},{"issue":"10","key":"10.1016\/j.engappai.2025.113495_b22","first-page":"1719","article-title":"Hierarchical extended parameter identification methods and convergence for finite impulse response moving average models based on the hierarchical identification principle","volume":"240","author":"Ding","year":"2025","journal-title":"Proc. Inst. Mech. Eng. Part I-J. Syst. Control. Eng."},{"issue":"5","key":"10.1016\/j.engappai.2025.113495_b23","doi-asserted-by":"crossref","first-page":"3078","DOI":"10.1109\/TAC.2022.3188478","article-title":"Recursive identification of time-varying Hammerstein systems with matrix forgetting","volume":"68","author":"Dokoupil","year":"2023","journal-title":"IEEE Trans. Autom. Control"},{"issue":"4","key":"10.1016\/j.engappai.2025.113495_b24","doi-asserted-by":"crossref","first-page":"6767","DOI":"10.1109\/TNNLS.2024.3385407","article-title":"Selective memory recursive least squares: Recast forgetting into memory in RBF neural network-based real-time learning","volume":"36","author":"Fei","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"9\u201310","key":"10.1016\/j.engappai.2025.113495_b25","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1016\/j.mcm.2010.01.003","article-title":"Hierarchical least squares based iterative identification for multivariable systems with moving average noises","volume":"51","author":"Han","year":"2010","journal-title":"Math. Comput. Modelling"},{"issue":"11","key":"10.1016\/j.engappai.2025.113495_b26","doi-asserted-by":"crossref","first-page":"16853","DOI":"10.1109\/TPEL.2025.3588296","article-title":"Adaptive linear time-varying parameter-varying modeling of lithium-ion batteries considering aging phenomenon","volume":"40","author":"Hou","year":"2025","journal-title":"IEEE Trans. Power Electron."},{"key":"10.1016\/j.engappai.2025.113495_b27","article-title":"Recursive identification with multiple forgetting factors for time-varying wireless power transfer systems","author":"Hou","year":"2025","journal-title":"IEEE J. Emerg. Sel. Top. Power Electron."},{"issue":"7","key":"10.1016\/j.engappai.2025.113495_b28","doi-asserted-by":"crossref","first-page":"7268","DOI":"10.1109\/TIE.2022.3199931","article-title":"Bias-correction errors-in-variables Hammerstein model identification","volume":"70","author":"Hou","year":"2023","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"4","key":"10.1016\/j.engappai.2025.113495_b29","doi-asserted-by":"crossref","first-page":"2292","DOI":"10.1109\/TSMC.2022.3213809","article-title":"Consistent subspace identification of errors-in-variables Hammerstein systems","volume":"53","author":"Hou","year":"2023","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"10.1016\/j.engappai.2025.113495_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2025.105157","article-title":"TMSF: Taylor expansion approximation network with multi-stage feature representation for optical flow estimation","volume":"162","author":"Huang","year":"2025","journal-title":"Digit. Signal Process."},{"issue":"8","key":"10.1016\/j.engappai.2025.113495_b31","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1007\/s11760-017-1105-8","article-title":"Iterative weighted nuclear norm for X-ray cardiovascular angiogram image denoising","volume":"11","author":"Huang","year":"2017","journal-title":"Signal Image Video Process."},{"issue":"5","key":"10.1016\/j.engappai.2025.113495_b32","doi-asserted-by":"crossref","first-page":"4831","DOI":"10.1109\/TCSVT.2024.3524794","article-title":"T2EA: Target-aware taylor expansion approximation network for infrared and visible image fusion","volume":"35","author":"Huang","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"1","key":"10.1016\/j.engappai.2025.113495_b33","article-title":"Modeling of human action recognition using hyperparameter tuned deep learning model","volume":"32","author":"Jain","year":"2023","journal-title":"J. Electron. Imaging"},{"issue":"1","key":"10.1016\/j.engappai.2025.113495_b34","first-page":"206","article-title":"Filtering-based accelerated estimation approach for generalized time-varying systems with disturbances and colored noises","volume":"70","author":"Ji","year":"2023","journal-title":"IEEE Trans. Circuits Syst. II: Express Briefs"},{"issue":"5","key":"10.1016\/j.engappai.2025.113495_b35","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1016\/j.jfranklin.2022.01.032","article-title":"Model recovery for multi-input signal-output nonlinear systems based on the compressed sensing recovery theory","volume":"359","author":"Ji","year":"2022","journal-title":"J. Franklin Inst."},{"key":"10.1016\/j.engappai.2025.113495_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.jprocont.2023.103007","article-title":"An identification algorithm of generalized time-varying systems based on the taylor series expansion and applied to a pH process","volume":"128","author":"Ji","year":"2023","journal-title":"J. Process Control"},{"issue":"9","key":"10.1016\/j.engappai.2025.113495_b37","doi-asserted-by":"crossref","first-page":"3727","DOI":"10.1002\/rnc.4961","article-title":"Parameter estimation for block-oriented nonlinear systems using the key term separation","volume":"30","author":"Ji","year":"2020","journal-title":"Internat. J. Robust Nonlinear Control"},{"key":"10.1016\/j.engappai.2025.113495_b38","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/8205929","article-title":"Multisource heterogeneous data fusion analysis of regional digital construction based on machine learning","volume":"2022","author":"Jiang","year":"2022","journal-title":"J. Sensors"},{"issue":"1","key":"10.1016\/j.engappai.2025.113495_b39","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/TEC.2021.3093010","article-title":"Model predictive control with a cascaded Hammerstein neural network of a wind turbine providing frequency containment reserve","volume":"37","author":"Kayedpour","year":"2022","journal-title":"IEEE Trans. Energy Convers."},{"key":"10.1016\/j.engappai.2025.113495_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpowsour.2025.237665","article-title":"Optimizing the effect of battery relaxation on electrochemical impedance spectroscopy measurement for real-time SOC estimation using transfer learning","volume":"654","author":"Li","year":"2025","journal-title":"J. Power Sources"},{"key":"10.1016\/j.engappai.2025.113495_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.113682","article-title":"Identification of the silverbox benchmark using extended polynomial kernel-based nonlinear observer canonical models","volume":"243","author":"Li","year":"2026","journal-title":"Mech. Syst. Signal Process."},{"issue":"5","key":"10.1016\/j.engappai.2025.113495_b42","first-page":"2774","article-title":"Parameter identification of the RBF-ARX model based on the hybrid whale optimization algorithm","volume":"71","author":"Li","year":"2024","journal-title":"IEEE Trans. Circuits Syst. II: Express Briefs"},{"key":"10.1016\/j.engappai.2025.113495_b43","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.apm.2021.10.032","article-title":"A new adaptive identification framework for nonlinear multi-input multi-output systems under colored noise","volume":"103","author":"Li","year":"2022","journal-title":"Appl. Math. Model."},{"key":"10.1016\/j.engappai.2025.113495_b44","doi-asserted-by":"crossref","DOI":"10.1080\/00295450.2025.2472543","article-title":"Sensitivity analysis of space parameters for integrated natural circulation reactor under the rolling condition coupling power increase","author":"Lian","year":"2025","journal-title":"Nuclear Technol."},{"issue":"3","key":"10.1016\/j.engappai.2025.113495_b45","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.automatica.2013.12.025","article-title":"An efficient hierarchical identification method for general dual-rate sampled-data systems","volume":"50","author":"Liu","year":"2014","journal-title":"Automatica"},{"key":"10.1016\/j.engappai.2025.113495_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.automatica.2022.110365","article-title":"Expectation\u2013maximization algorithm for bilinear systems by using the rauch-tung-striebel smoother","volume":"142","author":"Liu","year":"2022","journal-title":"Automatica"},{"key":"10.1016\/j.engappai.2025.113495_b47","doi-asserted-by":"crossref","DOI":"10.1007\/s00034-025-03327-y","article-title":"Greedy orthogonal least squares identification for multivariable Hammerstein models","author":"Liu","year":"2025","journal-title":"Circuits Systems Signal Process."},{"issue":"5","key":"10.1016\/j.engappai.2025.113495_b48","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1007\/s00707-025-04314-9","article-title":"Evaluation of mechanical behavior of textile microfibers","volume":"236","author":"Liu","year":"2025","journal-title":"Acta Mechanica"},{"issue":"13","key":"10.1016\/j.engappai.2025.113495_b49","doi-asserted-by":"crossref","DOI":"10.1587\/elex.20.20230141","article-title":"MA-GRNN: A high-efficient modeling attack approach utilizing generalized regression neural network for xor arbiter physical unclonable functions","volume":"20","author":"Liu","year":"2023","journal-title":"IEICE Electronics Express"},{"key":"10.1016\/j.engappai.2025.113495_b50","article-title":"Development of a neural network-based compensatory enhanced-Hammerstein modeling strategy for piezoelectric system with hysteresis","volume":"74","author":"Liu","year":"2025","journal-title":"IEEE Trans. Transp. Electrif."},{"issue":"1","key":"10.1016\/j.engappai.2025.113495_b51","article-title":"Probability-based identification of Hammerstein systems with asymmetric noise characteristics","volume":"73","author":"Liu","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"2","key":"10.1016\/j.engappai.2025.113495_b52","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1002\/rnc.7007","article-title":"Two-stage and three-stage recursive gradient identification of Hammerstein nonlinear systems based on the key term separation","volume":"34","author":"Lv","year":"2024","journal-title":"Internat. J. Robust Nonlinear Control"},{"issue":"18","key":"10.1016\/j.engappai.2025.113495_b53","doi-asserted-by":"crossref","first-page":"3040","DOI":"10.1049\/iet-cta.2019.0112","article-title":"Partially-coupled least squares based iterative parameter estimation for multi-variable output-error-like autoregressive moving average systems","volume":"13","author":"Ma","year":"2019","journal-title":"IET Control Theory Appl."},{"key":"10.1016\/j.engappai.2025.113495_b54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2023.3328685","article-title":"Identification of cellular measurements: A neural network approach","volume":"73","author":"Makled","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2025.113495_b55","doi-asserted-by":"crossref","DOI":"10.1002\/acs.4062","article-title":"Multi-innovation recursive methods for a class of nonlinear time series models based on the penalty term","author":"Niu","year":"2025","journal-title":"Internat. J. Adapt. Control Signal Process."},{"issue":"1","key":"10.1016\/j.engappai.2025.113495_b56","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1007\/s12555-021-1018-8","article-title":"Gradient-based parameter estimation for an exponential nonlinear autoregressive time-series model by using the multi-innovation","volume":"21","author":"Pan","year":"2023","journal-title":"Int. J. Control Autom. Syst."},{"issue":"12","key":"10.1016\/j.engappai.2025.113495_b57","doi-asserted-by":"crossref","first-page":"3940","DOI":"10.1007\/s12555-021-0845-y","article-title":"Hierarchical recursive least squares estimation algorithm for secondorder Volterra nonlinear systems","volume":"20","author":"Pan","year":"2022","journal-title":"Int. J. Control Autom. Syst."},{"issue":"7","key":"10.1016\/j.engappai.2025.113495_b58","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1049\/iet-spr.2019.0481","article-title":"Recursive coupled projection algorithms for multivariable output-error-like systems with coloured noises","volume":"14","author":"Pan","year":"2020","journal-title":"IET Signal Process."},{"issue":"5","key":"10.1016\/j.engappai.2025.113495_b59","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1007\/s12555-022-0253-y","article-title":"Multivariable CAR-like system identification with multi-innovation gradient and least squares algorithms","volume":"21","author":"Pan","year":"2023","journal-title":"Int. J. Control Autom. Syst."},{"issue":"5","key":"10.1016\/j.engappai.2025.113495_b60","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1109\/TAC.2023.3294101","article-title":"Recurrent equilibrium networks: Flexible dynamic models with guaranteed stability and robustness","volume":"69","author":"Revay","year":"2024","journal-title":"IEEE Trans. Autom. Control"},{"key":"10.1016\/j.engappai.2025.113495_b61","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.automatica.2016.05.024","article-title":"Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model","volume":"71","author":"Wang","year":"2016","journal-title":"Automatica"},{"key":"10.1016\/j.engappai.2025.113495_b62","doi-asserted-by":"crossref","DOI":"10.1016\/j.aml.2024.109207","article-title":"Key-term separation based hierarchical gradient approach for NN based Hammerstein battery model","volume":"157","author":"Wang","year":"2024","journal-title":"Appl. Math. Lett."},{"key":"10.1016\/j.engappai.2025.113495_b63","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2024.114555","article-title":"An innovative square root-untraced Kalman filtering strategy with full-parameter online identification for state of power evaluation of lithium-ion batteries","volume":"104","author":"Wang","year":"2024","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.engappai.2025.113495_b64","doi-asserted-by":"crossref","first-page":"157","DOI":"10.23919\/PCMP.2023.000257","article-title":"Improved multiple feature-electrochemical thermal coupling modeling of lithium-ion batteries at low-temperature with real-time coefficient correction","volume":"9","author":"Wang","year":"2024","journal-title":"Prot. Control. Mod. Power Syst."},{"key":"10.1016\/j.engappai.2025.113495_b65","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107503","article-title":"Accurate identification of anxiety and depression based on the dlpfc in an emotional autobiographical memory task: A machine learning approach","volume":"104","author":"Wang","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"issue":"7","key":"10.1016\/j.engappai.2025.113495_b66","first-page":"1222","article-title":"Iterative parameter estimation for a class of fractional-order Hammerstein nonlinear systems disturbed by colored noise","volume":"239","author":"Wang","year":"2025","journal-title":"Proc. Inst. Mech. Eng. I: J. Syst. Control. Eng."},{"issue":"17","key":"10.1016\/j.engappai.2025.113495_b67","doi-asserted-by":"crossref","DOI":"10.1016\/j.jfranklin.2025.108143","article-title":"Hierarchical maximum likelihood multi-innovation identification methods for a class of multivariable hammerstein-input-nonlinear systems","volume":"362","author":"Wang","year":"2025","journal-title":"J. Franklin Inst."},{"key":"10.1016\/j.engappai.2025.113495_b68","doi-asserted-by":"crossref","DOI":"10.1016\/j.sysconle.2025.106094","article-title":"Highly efficient three-stage maximum likelihood recursive least squares identification method for multiple-input multiple-output systems","volume":"200","author":"Wang","year":"2025","journal-title":"Systems Control Lett."},{"issue":"3","key":"10.1016\/j.engappai.2025.113495_b69","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1002\/oca.3257","article-title":"Auxiliary model-based maximum likelihood multi-innovation forgetting gradient identification for a class of multivariable systems","volume":"46","author":"Wang","year":"2025","journal-title":"Opt. Control Appl. Methods"},{"issue":"8","key":"10.1016\/j.engappai.2025.113495_b70","doi-asserted-by":"crossref","first-page":"5608","DOI":"10.1007\/s00034-025-03068-y","article-title":"Highly efficient two-stage filtering-based maximum likelihood stochastic gradient algorithm for multiple-input multiple-output systems","volume":"44","author":"Wang","year":"2025","journal-title":"Circuits Systems Signal Process."},{"key":"10.1016\/j.engappai.2025.113495_b71","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112912","article-title":"A robust filter and smoother-based expectation\u2013maximization algorithm for bilinear systems with heavy-tailed noise","volume":"236","author":"Wang","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2025.113495_b72","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.isatra.2024.10.028","article-title":"Online estimation method for extreme learning machine with kernels based on the multi-innovation theory and intelligent optimization strategy","volume":"156","author":"Wang","year":"2025","journal-title":"ISA Trans."},{"key":"10.1016\/j.engappai.2025.113495_b73","article-title":"Parameter estimation method for separable fractional-order Hammerstein nonlinear systems based on the on-line measurements","volume":"488","author":"Wang","year":"2025","journal-title":"Appl. Math. Comput."},{"issue":"1","key":"10.1016\/j.engappai.2025.113495_b74","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1002\/acs.3923","article-title":"Identification of a non-commensurate fractional-order nonlinear system based on the separation scheme","volume":"39","author":"Wang","year":"2025","journal-title":"Internat. J. Adapt. Control Signal Process."},{"issue":"13","key":"10.1016\/j.engappai.2025.113495_b75","doi-asserted-by":"crossref","first-page":"5364","DOI":"10.1002\/rnc.7986","article-title":"The Aitken accelerated gradient algorithm for a class of dual-rate volterra nonlinear systems utilizing the self-organizing map technique","volume":"35","author":"Wang","year":"2025","journal-title":"Internat. J. Robust Nonlinear Control"},{"key":"10.1016\/j.engappai.2025.113495_b76","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2023.106831","article-title":"A hierarchical adaptive extended Kalman filter algorithm for lithium-ion battery state of charge estimation","volume":"62","author":"Wang","year":"2023","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.engappai.2025.113495_b77","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpowsour.2024.235594","article-title":"An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature Kalman filter","volume":"624","author":"Wang","year":"2024","journal-title":"J. Power Sources"},{"issue":"4","key":"10.1016\/j.engappai.2025.113495_b78","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1007\/s12555-022-1002-y","article-title":"Iterative algorithm for feedback nonlinear systems by using the maximum likelihood principle","volume":"22","author":"Xia","year":"2024","journal-title":"Int. J. Control. Autom. Syst."},{"issue":"3","key":"10.1016\/j.engappai.2025.113495_b79","doi-asserted-by":"crossref","first-page":"1864","DOI":"10.1002\/rnc.7065","article-title":"Maximum likelihood gradient-based iterative estimation for closed-loop Hammerstein nonlinear systems","volume":"34","author":"Xia","year":"2024","journal-title":"Internat. J. Robust Nonlinear Control"},{"issue":"11","key":"10.1016\/j.engappai.2025.113495_b80","doi-asserted-by":"crossref","first-page":"2983","DOI":"10.1002\/acs.3669","article-title":"Hierarchical recursive least squares parameter estimation methods for multiple-input multiple-output systems by using the auxiliary models","volume":"37","author":"Xing","year":"2023","journal-title":"Internat. J. Adapt. Control Signal Process."},{"key":"10.1016\/j.engappai.2025.113495_b81","doi-asserted-by":"crossref","DOI":"10.1016\/j.cam.2023.115687","article-title":"Auxiliary model-based hierarchical stochastic gradient methods for multiple-input multiple-output systems","volume":"442","author":"Xing","year":"2024","journal-title":"J. Comput. Appl. Math."},{"key":"10.1016\/j.engappai.2025.113495_b82","doi-asserted-by":"crossref","DOI":"10.1016\/j.sysconle.2024.105762","article-title":"Highly-efficient filtered hierarchical identification algorithms for multiple-input multiple-output systems with colored noises","volume":"186","author":"Xing","year":"2024","journal-title":"Systems Control Lett."},{"issue":"1","key":"10.1016\/j.engappai.2025.113495_b83","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1002\/rnc.5266","article-title":"Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems","volume":"31","author":"Xu","year":"2021","journal-title":"Internat J Robust Nonlinear Control"},{"key":"10.1016\/j.engappai.2025.113495_b84","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1109\/LSP.2022.3152108","article-title":"Joint parameter and time-delay estimation for a class of nonlinear time-series models","volume":"29","author":"Xu","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"key":"10.1016\/j.engappai.2025.113495_b85","article-title":"Separable synchronous multi-innovation gradient-based iterative signal modeling from on-line measurements","volume":"71","author":"Xu","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2025.113495_b86","doi-asserted-by":"crossref","DOI":"10.1016\/j.sysconle.2024.105774","article-title":"Novel parameter estimation method for the systems with colored noises by using the filtering identification idea","volume":"186","author":"Xu","year":"2024","journal-title":"Systems Control Lett."},{"issue":"6","key":"10.1016\/j.engappai.2025.113495_b87","doi-asserted-by":"crossref","first-page":"3718","DOI":"10.1007\/s00034-024-02627-z","article-title":"Adaptive multi-innovation gradient identification algorithms for a controlled autoregressive autoregressive moving average model","volume":"43","author":"Xu","year":"2024","journal-title":"Circuits Systems Signal Process."},{"issue":"1","key":"10.1016\/j.engappai.2025.113495_b88","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1002\/acs.3699","article-title":"Decomposition and composition modeling algorithms for control systems with colored noises","volume":"38","author":"Xu","year":"2024","journal-title":"Internat. J. Adapt. Control Signal Process."},{"issue":"16","key":"10.1016\/j.engappai.2025.113495_b89","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.1080\/00207721.2024.2375615","article-title":"The filtering-based recursive least squares identification and convergence analysis for nonlinear feedback control systems with coloured noises","volume":"55","author":"Xu","year":"2024","journal-title":"Int. J. Syst. Sci."},{"key":"10.1016\/j.engappai.2025.113495_b90","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1109\/LSP.2025.3567804","article-title":"A novel three-stage filtering identification algorithm for the exponential autoregressive time-series model","volume":"32","author":"Xu","year":"2025","journal-title":"IEEE Signal Process, Lett."},{"key":"10.1016\/j.engappai.2025.113495_b91","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/LSP.2024.3519897","article-title":"A delta operator state estimation algorithm for discrete-time systems with state time-delay","volume":"32","author":"Xu","year":"2025","journal-title":"IEEE Signal Process. Lett."},{"key":"10.1016\/j.engappai.2025.113495_b92","article-title":"Kalman-based joint estimation for generalized time-varying parameter systems with the unknown invariant matrix","author":"Xu","year":"2025","journal-title":"IEEE Trans. Cybern."},{"issue":"6","key":"10.1016\/j.engappai.2025.113495_b93","doi-asserted-by":"crossref","first-page":"2074","DOI":"10.1002\/acs.3792","article-title":"Online identification of Hammerstein systems with B-spline networks","volume":"38","author":"Y.J.","year":"2024","journal-title":"Internat. J. Adapt. Control Signal Process."},{"issue":"3","key":"10.1016\/j.engappai.2025.113495_b94","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.camwa.2010.12.014","article-title":"Hierarchical gradient based iterative parameter estimation algorithm for multivariable output error moving average systems","volume":"61","author":"Zhang","year":"2011","journal-title":"Comput. Math. Appl."},{"issue":"2","key":"10.1016\/j.engappai.2025.113495_b95","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1007\/s11071-020-05865-3","article-title":"Nonlinear control for soliton interactions in optical fiber systems","volume":"101","author":"Zhang","year":"2020","journal-title":"Nonlinear Dyn."},{"issue":"5","key":"10.1016\/j.engappai.2025.113495_b96","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1002\/oca.3321","article-title":"Auxiliary model maximum likelihood moving-data-window generalized extended gradient-based iterative algorithm for multivariable autoregressive output-error autoregressive moving-average systems","volume":"46","author":"Zhang","year":"2025","journal-title":"Optimal Control Appl. Methods"},{"issue":"3","key":"10.1016\/j.engappai.2025.113495_b97","first-page":"441","article-title":"Auxiliary model maximum likelihood least squares-based iterative algorithm for multivariable autoregressive output-error autoregressive moving average systems","volume":"239","author":"Zhang","year":"2025","journal-title":"Proc. Inst. Mech. Eng. Part I-J. Syst. Control. Eng."},{"issue":"12","key":"10.1016\/j.engappai.2025.113495_b98","doi-asserted-by":"crossref","first-page":"16514","DOI":"10.1109\/TIE.2024.3390739","article-title":"Neural network\/PID adaptive compound control based on RBFNN identification modeling for an aerial inertially stabilized platform","volume":"71","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"8","key":"10.1016\/j.engappai.2025.113495_b99","doi-asserted-by":"crossref","DOI":"10.1007\/s11082-024-07280-z","article-title":"Spatiotemporal soliton solutions in three dimensional combined linear-harmonic potentials with varying sources","volume":"56","author":"Zhou","year":"2024","journal-title":"Opt. Quantum Electronics"},{"key":"10.1016\/j.engappai.2025.113495_b100","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2025.3617413","article-title":"Dynamic factor and multi-innovation-based output-input feedback elman network modeling from measurements","volume":"74","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2025.113495_b101","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.patrec.2024.03.009","article-title":"PDTE: Pyramidal deep taylor expansion for optical flow estimation","volume":"180","author":"Zhu","year":"2024","journal-title":"Pattern Recogn. Lett."},{"issue":"21","key":"10.1016\/j.engappai.2025.113495_b102","doi-asserted-by":"crossref","first-page":"26357","DOI":"10.1109\/JSEN.2023.3318371","article-title":"Rfrflow: Recurrent feature refinement network for optical flow estimation","volume":"23","author":"Zhu","year":"2023","journal-title":"IEEE Sensors J."},{"issue":"4","key":"10.1016\/j.engappai.2025.113495_b103","doi-asserted-by":"crossref","first-page":"5053","DOI":"10.1109\/JSEN.2023.3348238","article-title":"GTEA: Guided taylor expansion approximation network for optical flow estimation","volume":"24","author":"Zhu","year":"2024","journal-title":"IEEE Sensors J."},{"key":"10.1016\/j.engappai.2025.113495_b104","doi-asserted-by":"crossref","DOI":"10.1002\/cta.70128","article-title":"Parameter identification and open-circuit voltage estimation of lithium-ion battery circuit model based on multi-innovation theory","author":"Zou","year":"2025","journal-title":"Int. J. Circuit Theory Appl."},{"key":"10.1016\/j.engappai.2025.113495_b105","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.ijhydene.2025.03.171","article-title":"Comparative analysis of hydrogen production methods: Environmental impact and efficiency of electrochemical and thermochemical processes","volume":"118","author":"Zuo","year":"2025","journal-title":"Int. J. Hydrog. Energy"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197625035262?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197625035262?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T02:38:01Z","timestamp":1773887881000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197625035262"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":105,"alternative-id":["S0952197625035262"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2025.113495","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A dynamic forgetting factor-based recursive estimation framework for radial basis function-based Hammerstein battery models","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2025.113495","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113495"}}