Parameters estimation of Hammerstein–Wiener time-delay nonlinear model

A Supervised Bidirectional Long Short-Term Memory Network for Data-Driven Dynamic Soft Sensor Modeling

Lui, C. F., Liu, Y., & Xie, M. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3152856 · Cited by 188 · see citing works

A hierarchical adaptive extended Kalman filter algorithm for lithium-ion battery state of charge estimation

Wang, D., Yang, Y. J., & Gu, T. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.106831 · Cited by 138 · see citing works

Hybrid improved particle swarm optimization-cuckoo search optimized fuzzy PID controller for micro gas turbine

Yang, R., Liu, Y., Yu, Y., et al. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.08.120 · Cited by 67 · see citing works

Real-Time Reachable Set Control for Neutral Singular Markov Jump Systems With Mixed Delays

Zhang, L., Zhao, X., & Zhao, N. (2021) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2021.3118075 · Cited by 65 · see citing works

Short-Term Traffic Flow Forecasting Using Ensemble Approach Based on Deep Belief Networks

Jin, L., Wu, N., Qiao, Y., et al. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3011700 · Cited by 61 · see citing works

Estimation of Turbidity in Water Treatment Plant using Hammerstein-Wiener and Neural Network Technique

Gaya, M. S., Yusuf, L. A., Mustapha, M., et al. (2017) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v5.i3.pp666-672 · Cited by 60 · see citing works

State of charge and state of health diagnosis of batteries with voltage-controlled models

Braun, J. A., Behmann, R., Schmider, D., & Bessler, W. G. (2022) · Journal of Power Sources · doi:10.1016/j.jpowsour.2022.231828 · Cited by 55 · see citing works

Iterative identification of nonlinear dynamic systems with output backlash using three-block cascade models

Vörös, J. (2014) · Nonlinear Dynamics · doi:10.1007/s11071-014-1804-4 · Cited by 54 · see citing works

A novel extreme learning Machine-based Hammerstein-Wiener model for complex nonlinear industrial processes

Xu, K., Yang, H., & Zhu, C. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.05.049 · Cited by 54 · see citing works

A general radial basis function neural network assisted hybrid modeling method for photovoltaic cell operating temperature prediction

Dong, X.-J., Shen, J.-N., He, G., et al. (2021) · Energy · doi:10.1016/j.energy.2021.121212 · Cited by 54 · see citing works

Nonlinear system modeling using self-organizing fuzzy neural networks for industrial applications

Zhou, H., Zhao, H., & Zhang, Y. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01645-z · Cited by 53 · see citing works

Optimal forecast combination based on PSO-CS approach for daily agricultural future prices forecasting

Zeng, L., Ling, L., Zhang, D., & Jiang, W. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109833 · Cited by 52 · see citing works

A bias-correction modeling method of Hammerstein–Wiener systems with polynomial nonlinearities using noisy measurements

Hou, J., Wang, H., Su, H., et al. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111329 · Cited by 52 · see citing works

A multi-information fusion “triple variables with iteration” inertia weight PSO algorithm and its application

Li, M., Chen, H., Shi, X. L., et al. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105677 · Cited by 50 · see citing works

Data-driven predictive control of Hammerstein–Wiener systems based on subspace identification

Luo, X., & Song, Y. (2017) · Information Sciences · doi:10.1016/j.ins.2017.09.004 · Cited by 48 · see citing works

Parameter Estimation of Wiener Systems Based on the Particle Swarm Iteration and Gradient Search Principle

Li, J., Zong, T., Gu, J., et al. (2020) · Circuits Systems and Signal Processing · doi:10.1007/s00034-019-01329-1 · Cited by 47 · see citing works

Bias compensation‐based parameter and state estimation for a class of time‐delay non‐linear state‐space models

Gu, Y., Zhu, Q., & Nouri, H. (2020) · IET Control Theory and Applications · doi:10.1049/iet-cta.2020.0104 · Cited by 45 · see citing works

Identification method of neuro‐fuzzy‐based Hammerstein model with coloured noise

Li, F., Jia, L., & Peng, D. (2017) · IET Control Theory and Applications · doi:10.1049/iet-cta.2017.0306 · Cited by 42 · see citing works

A novel data-driven method for online parameter identification of an electrochemical model based on cuckoo search and particle swarm optimization algorithm

Huang, S., Lin, N., Wang, Z., Zhang, Z., Wen, S., Zhao, Y., & Li, Q. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.234261 · Cited by 42 · see citing works

Identification scheme for fractional Hammerstein models with the delayed Haar wavelet

Kothari, K., Mehta, U., Prasad, V., et al. (2020) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003093 · Cited by 40 · see citing works

Identification for nonlinear systems modelled by deep long short-term memory networks based Wiener model

Li, F., Yang, Y., & Xia, Y. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111631 · Cited by 40 · see citing works

Modeling and parametric identification of Hammerstein systems with time delay and asymmetric dead-zones using fractional differential equations

Prasad, V., & Mehta, U. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108568 · Cited by 39 · see citing works

Data-Driven Hybrid Neural Fuzzy Network and ARX Modeling Approach to Practical Industrial Process Identification

Li, F., Zheng, T., He, N., et al. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105821 · Cited by 39 · see citing works

FREQUENCY IDENTIFICATION OF HAMMERSTEIN-WIENER SYSTEMS WITH PIECEWISE AFFINE INPUT NONLINEARITY

Giri, F., Brouri, A., Amdouri, O., et al. (2014) · IFAC Proceedings Volumes · doi:10.3182/20140824-6-za-1003.00303 · Cited by 34 · see citing works

Decomposition‐based over‐parameterization forgetting factor stochastic gradient algorithm for Hammerstein‐Wiener nonlinear systems with non‐uniform sampling

Liu, Q., Xiao, Y., Ding, F., et al. (2021) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.5576 · Cited by 32 · see citing works

Robust Data-Driven Iterative Learning Control for Linear-Time-Invariant and Hammerstein–Wiener Systems

Dong, J. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3105745 · Cited by 30 · see citing works

Parameter Learning for the Nonlinear System Described by a Class of Hammerstein Models

Li, F., Zhu, X., & Cao, Q. (2022) · Circuits Systems and Signal Processing · doi:10.1007/s00034-022-02240-y · Cited by 30 · see citing works

Identification of Wiener model using step signals and particle swarm optimization

Tang, Y., Qiao, L., & Guan, X. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.10.008 · Cited by 26 · see citing works

Adversarial Training-Based Deep Layer-Wise Probabilistic Network for Enhancing Soft Sensor Modeling of Industrial Processes

Xie, Y., Wang, J., Xie, S., & Chen, X. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3322195 · Cited by 26 · see citing works

A hypervolume-based cuckoo search algorithm with enhanced diversity and adaptive scaling factor

Liang, M., Wang, L., Wang, L., et al. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.111073 · Cited by 21 · see citing works

Advances in Tracking Control for Piezoelectric Actuators Using Fuzzy Logic and Hammerstein-Wiener Compensation

Napole, C., Barambones, Ó., Calvo, I., et al. (2020) · Mathematics · doi:10.3390/math8112071 · Cited by 19 · see citing works

Identification of Wiener Time Delay Systems Based on Hierarchical Gradient Approach

Atitallah, A., Bedoui, S., & Abderrahim, K. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.05.040 · Cited by 18 · see citing works

Phase-based order separation for Volterra series identification

Bouvier, D., Hélie, T., & Roze, D. (2019) · International Journal of Control · doi:10.1080/00207179.2019.1694175 · Cited by 16 · see citing works

Adaptive TS fuzzy MPC based on Particle Swarm Optimization-Cuckoo Search algorithm

Taieb, A., Salhi, H., & Châari, A. (2022) · ISA Transactions · doi:10.1016/j.isatra.2022.05.018 · Cited by 16 · see citing works

Parameter estimation for the Hammerstein-Wiener nonlinear system and application in lithium-ion batteries

Li, F., Zhou, S., & Liu, R. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.114265 · Cited by 15 · see citing works

Identification of non-linear stochastic systems using a new Hammerstein-Wiener neural network: a simulation study through a non-linear hydraulic process

Abouda, S. E., Abid, D. B. H., Elloumi, M., et al. (2020) · International Journal of Computer Applications in Technology · doi:10.1504/ijcat.2020.109340 · Cited by 11 · see citing works

An RNN-Based Adaptive Hybrid Time Series Forecasting Model for Driving Data Prediction

Seo, J. H., & Kim, K.-D. (2025) · IEEE Access · doi:10.1109/access.2025.3554803 · Cited by 10 · see citing works

Identification of Nonlinear Dynamic Systems Using Fuzzy Hammerstein-Wiener Systems

Abouda, S. E., Abid, D. B. H., Elloumi, M., et al. (2019) · International Conference on Sciences and Techniques of Automatic Control and Computer Engineering · doi:10.1109/sta.2019.8717218 · Cited by 9 · see citing works

Finite‐time adaptive fuzzy tracking control for high‐order nonlinear time‐delay systems with dead‐zone

Lu, K., & Wang, H. (2023) · IET Control Theory and Applications · doi:10.1049/cth2.12566 · Cited by 9 · see citing works

Identification of a precision motion stage based on the Hammerstein-Wiener model

Zhang, Z., Zhang, D., Zheng, H., et al. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8865318 · Cited by 6 · see citing works

Identification of Hammerstein-Wiener model with discontinuous input nonlinearity

Brouri, A., Mansouri, F. E. E., Chaoui, F. Z., et al. (2023) · Science China Information Sciences · doi:10.1007/s11432-022-3767-2 · Cited by 6 · see citing works

Parameter Identification for the Hammerstein-Wiener Nonlinear Time Delay Systems with Process Noises

Li, F., Han, J., He, N., et al. (2024) · Circuits Systems and Signal Processing · doi:10.1007/s00034-024-02900-1 · Cited by 6 · see citing works

Simultaneous identification of parameters of fractional-order Hammerstein-Wiener model with time-delay using operational matrix in a colored noise environment

Liu, C., Wang, H., & Zhang, Q. (2024) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2024.107444 · Cited by 4 · see citing works

Parameters estimation for the Hammerstein‐Wiener models with colored noise based on hybrid signals

Li, F., & Han, J. (2023) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.3735 · Cited by 3 · see citing works

Bearing Dynamics Modeling Based on the Virtual State-Space and Hammerstein–Wiener Model

Jiang, G., Zhou, K., Li, Z., et al. (2024) · Sensors · doi:10.3390/s24165410 · Cited by 3 · see citing works

Virtual Metrology for Multistage Processes Using Variational Inference Gaussian Mixture Model and Extreme Learning Machine

Pan, T., Liu, L., & Li, M. (2024) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2024.3392898 · Cited by 2 · see citing works

Identification of batch reactor process using Extreme Learning Machine based Hammerstein and Hammerstein-Wiener Models

Balakrishnan, M., Rajendran, V., Prajwal Shettigar, J., et al. (2024) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2818/1/012027 · Cited by 1 · see citing works