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Journal articles on the topic 'Grey-box modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Bidarvatan, M., V. Thakkar, M. Shahbakhti, B. Bahri, and A. Abdul Aziz. "Grey-box modeling of HCCI engines." Applied Thermal Engineering 70, no. 1 (2014): 397–409. http://dx.doi.org/10.1016/j.applthermaleng.2014.05.031.

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2

Hasan, Md Moudud, Md Shariot Ullah, Ajoy Kumar Saha, and MG Mostofa Amin. "Comparing the performances of multiple rainfall-runoff models of a karst watershed." Asian-Australasian Journal of Bioscience and Biotechnology 6, no. 1 (2021): 26–39. http://dx.doi.org/10.3329/aajbb.v6i1.54878.

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Different modeling concepts, a simple (black-box) to a fully distributed modeling (white-box), were used to develop a rainfall-runoff model based on the watershed characteristics to estimate runoff at the watershed outlet. A conceptual (grey-box) model is usually a balance between the black-box and white-box model. In this study, three grey-box models were developed by varying model structures for a karst watershed. The performance of the grey-box models was evaluated and compared with a semi-distributed type (white-box) model that was developed using the Soil and Water Assessment Tool in a pr

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3

Halmschlager, Verena, Stefan Müllner, and René Hofmann. "Mechanistic Grey-Box Modeling of a Packed-Bed Regenerator for Industrial Applications." Energies 14, no. 11 (2021): 3174. http://dx.doi.org/10.3390/en14113174.

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Thermal energy storage is essential to compensate for energy peaks and troughs of renewable energy sources. However, to implement this storage in new or existing industries, robust and accurate component models are required. This work examines the development of a mechanistic grey-box model for a sensible thermal energy storage, a packed-bed regenerator. The mechanistic grey-box model consists of physical relations/equations and uses experimental data to optimize specific parameters of these equations. Using this approach, a basic model and two models with extensions I and II, which vary in th

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4

Green, Christy, and Srinivas Garimella. "Residential microgrid optimization using grey-box and black-box modeling methods." Energy and Buildings 235 (March 2021): 110705. http://dx.doi.org/10.1016/j.enbuild.2020.110705.

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5

Li, Kang, Steve Thompson, Gareth-Guan R. Duan, and Jian-xun Peng. "A CASE STUDY OF FUNDAMENTAL GREY-BOX MODELING." IFAC Proceedings Volumes 35, no. 1 (2002): 127–32. http://dx.doi.org/10.3182/20020721-6-es-1901.00432.

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6

Zhang, Xiang, Katia Ritosa, Dirk Saelens, and Staf Roels. "Comparing statistical modeling techniques for heat loss coefficient estimation using in-situ data." Journal of Physics: Conference Series 2069, no. 1 (2021): 012101. http://dx.doi.org/10.1088/1742-6596/2069/1/012101.

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Abstract The combination of in-situ collected data and statistical modelling techniques proved to be a promising approach in actual building energy performance assessments, such as heat loss coefficient (HLC) evaluation. In this study, based on datasets of co-heating and pseudo-random binary sequence heating tests on a portable site office, the performance of three types of statistical models (i.e. multiple linear regression (MLR), autoregressive with exogenous terms (ARX), and grey-box models) on HLC-determination are examined. It is revealed that a similar HLC estimation outcome (about 115 W

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Tanaka, Hideyuki, and Yoshito Ohta. "Grey-box modeling for mechanical systems in frequency domain." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2014 (May 5, 2014): 149–54. http://dx.doi.org/10.5687/sss.2014.149.

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8

Hellsen, R. H. A., G. Z. Angelis, M. J. G. van de Molengraft, A. G. de Jager, and J. J. Kok. "Grey-box Modeling of Friction: An Experimental Case-study." European Journal of Control 6, no. 3 (2000): 258–67. http://dx.doi.org/10.1016/s0947-3580(00)71134-4.

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9

Aghababaei, A., and M. Hexamer. "Grey-box Modeling of Ex-vivo Isolated Perfused Kidney." IFAC-PapersOnLine 48, no. 20 (2015): 171–76. http://dx.doi.org/10.1016/j.ifacol.2015.10.134.

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10

Trinh, Minh, Michael Königs, Lukas Gründel, Marcel Beier, Oliver Petrovic, and Christian Brecher. "Accuracy Optimization of Robotic Machining Using Grey-Box Modeling and Simulation Planning Assistance." Journal of Manufacturing and Materials Processing 9, no. 4 (2025): 126. https://doi.org/10.3390/jmmp9040126.

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The aim of this paper is to develop an approach to increase the accuracy of industrial robots for machining processes. During machining tasks, process forces displace the end effector of the robot. A simulation of the various process influences is therefore necessary to ensure stable machining during production planning in optimizing the process parameters. Realistic simulations require precise dynamics and stiffness models of the robot. Regarding the dynamics, the frictional component is highly complex and difficult to model. Therefore, this paper follows a grey-box approach to combine the ad

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11

Özkan, Leyla, Reinout Romijn, Siep Weiland, Wolfgang Marquardt, and Jobert Ludlage. "MODEL REDUCTION OF NONLINEAR SYSTEMS: A GREY-BOX MODELING APPROACH1." IFAC Proceedings Volumes 40, no. 12 (2007): 366–71. http://dx.doi.org/10.3182/20070822-3-za-2920.00061.

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12

Leifsson, Leifur Þ., Hildur Sævarsdóttir, Sven Þ. Sigurðsson, and Ari Vésteinsson. "Grey-box modeling of an ocean vessel for operational optimization." Simulation Modelling Practice and Theory 16, no. 8 (2008): 923–32. http://dx.doi.org/10.1016/j.simpat.2008.03.006.

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13

Karabacak, Kerim. "A Grey-Box Model of a DC/DC Boost Converter for PV Energy Systems." International Transactions on Electrical Energy Systems 2024 (March 20, 2024): 1–18. http://dx.doi.org/10.1155/2024/3559456.

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This paper presents a grey-box model of a DC/DC boost converter for PV energy systems. The proposed model contains a white-box model part and a black-box model part together to prepare a better model for the PV boost converter. The white-box model part is used for knowledge of the circuit by mathematical equations since the black-box model part is used for unknown parameters such as temperature and electromagnetic interference. The black-box part of the proposed model is created by a nonlinear system identification of a real boost converter circuit with an artificial neural network. The precis

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Buchaniec, Szymon, Marek Gnatowski, and Grzegorz Brus. "Integration of Classical Mathematical Modeling with an Artificial Neural Network for the Problems with Limited Dataset." Energies 14, no. 16 (2021): 5127. http://dx.doi.org/10.3390/en14165127.

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One of the most common problems in science is to investigate a function describing a system. When the estimate is made based on a classical mathematical model (white-box), the function is obtained throughout solving a differential equation. Alternatively, the prediction can be made by an artificial neural network (black-box) based on trends found in past data. Both approaches have their advantages and disadvantages. Mathematical models were seen as more trustworthy as their prediction is based on the laws of physics expressed in the form of mathematical equations. However, the majority of exis

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15

Romijn, Reinout, Leyla Özkan, Siep Weiland, Jobert Ludlage, and Wolfgang Marquardt. "A grey-box modeling approach for the reduction of nonlinear systems." Journal of Process Control 18, no. 9 (2008): 906–14. http://dx.doi.org/10.1016/j.jprocont.2008.06.007.

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16

Barzegari, Mohammad M., Ebrahim Alizadeh, and Amir H. Pahnabi. "Grey-box modeling and model predictive control for cascade-type PEMFC." Energy 127 (May 2017): 611–22. http://dx.doi.org/10.1016/j.energy.2017.03.160.

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17

Fjerbæk, Esben Visby, and Christian Anker Hviid. "Benchmarking Heating System Performance in Office Buildings through Grey-box Modeling." Journal of Physics: Conference Series 2654, no. 1 (2023): 012068. http://dx.doi.org/10.1088/1742-6596/2654/1/012068.

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Abstract The transition to renewable energy sources requires that larger shares of heating production should come from heat pumps both on individual level and in district heating networks. The efficiency of heat pumps is highly dependent on the temperature lift. Therefore, it is key to assess the possibilities of low-temperature heating in buildings. This paper proposes a data-driven methodology to analyse and benchmark the performance of radiator heating systems, by estimating parameters for the building’s envelope and heating system using Modelica with ModestPy. The methodology requires litt

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18

Xue, Yingfang, Chaozhi Cai, and Hui Xu. "Grey Box Modeling of Gas Temperature for a High-Speed and High-Temperature Heat-Airflow Test System." Applied Sciences 12, no. 24 (2022): 12883. http://dx.doi.org/10.3390/app122412883.

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Aiming at a problem that it is difficult for accurately obtaining the mathematical model of a gas temperature for a high-speed, high-temperature heat-airflow test system, a grey box modeling method combining first principle of modeling, recursive least squares identification with auxiliary variable and correlation analysis is proposed, and the precise mathematical model of gas temperature is established. Firstly, the preliminary mathematical model of gas temperature is obtained by using the first principle modeling method, and the dynamic behaviors of the system are analyzed; the prior knowled

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19

Mei, Bin, Licheng Sun, Guoyou Shi, and Xiaodong Liu. "Ship Maneuvering Prediction Using Grey Box Framework via Adaptive RM-SVM with Minor Rudder." Polish Maritime Research 26, no. 3 (2019): 115–27. http://dx.doi.org/10.2478/pomr-2019-0052.

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Abstract A grey box framework is applied to model ship maneuvering by using a reference model (RM) and a support vector machine (SVM) (RM-SVM). First, the nonlinear characteristics of the target ship are determined using the RM and the similarity rule. Then, the linear SVM adaptively fits the errors between acceleration variables of RM and target ship. Finally, the accelerations of the target ship are predicted using RM and linear SVM. The parameters of the RM are known and conveniently acquired, thus avoiding the modeling process. The SVM has the advantages of fast training, quick simulation,

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20

Barszcz, Tomasz, and Piotr Czop. "Estimation of feedwater heater parameters based on a grey-box approach." International Journal of Applied Mathematics and Computer Science 21, no. 4 (2011): 703–15. http://dx.doi.org/10.2478/v10006-011-0056-4.

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Estimation of feedwater heater parameters based on a grey-box approachThe first-principle modeling of a feedwater heater operating in a coal-fired power unit is presented, along with a theoretical discussion concerning its structural simplifications, parameter estimation, and dynamical validation. The model is a part of the component library of modeling environments, called the Virtual Power Plant (VPP). The main purpose of the VPP is simulation of power generation installations intended for early warning diagnostic applications. The model was developed in the Matlab/Simulink package. There ar

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21

Yu, Wen, and Francisco Vega. "Nonlinear system modeling using the takagi-sugeno fuzzy model and long-short term memory cells." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 4547–56. http://dx.doi.org/10.3233/jifs-200491.

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The data driven black-box or gray-box models like neural networks and fuzzy systems have some disadvantages, such as the high and uncertain dimensions and complex learning process. In this paper, we combine the Takagi-Sugeno fuzzy model with long-short term memory cells to overcome these disadvantages. This novel model takes the advantages of the interpretability of the fuzzy system and the good approximation ability of the long-short term memory cell. We propose a fast and stable learning algorithm for this model. Comparisons with others similar black-box and grey-box models are made, in orde

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22

Li, Yanfei, Zheng O'Neill, Liang Zhang, Jianli Chen, Piljae Im, and Jason DeGraw. "Grey-box modeling and application for building energy simulations - A critical review." Renewable and Sustainable Energy Reviews 146 (August 2021): 111174. http://dx.doi.org/10.1016/j.rser.2021.111174.

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23

ADACHI, Shuichi, and Tomoaki EDA. "Continuous-Time Grey-Box Modeling in Consideration of Deterministic a priori Knowledge." Transactions of the Society of Instrument and Control Engineers 32, no. 3 (1996): 417–19. http://dx.doi.org/10.9746/sicetr1965.32.417.

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24

Fjerbæk, Esben Visby, Kevin Michael Smith, and Christian Anker Hviid. "Grey-box modeling of Air Handling Units for Analysis and Virtual Sensing." E3S Web of Conferences 562 (2024): 10005. http://dx.doi.org/10.1051/e3sconf/202456210005.

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The optimization of control sequences in air handling units (AHUs) presents a significant opportunity for energy savings within HVAC systems. However, many building owners and operators require quantifiable estimates of potential energy savings before committing to retrofitting control systems. Valid estimates of energy savings require system models that consider capacity and limitations of the AHU, but in existing systems, scarce information hinders such modeling efforts. This lack of information complicates AHU modeling and the assessment of alternative control strategies. This paper demonst

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25

Mabrouk, M., M. A. Boujemaa, and F. Choubani. "Grey Box Non-Linearities Modeling and Characterization of a BandPass BAW Filter." Radioengineering 25, no. 2 (2016): 338–44. http://dx.doi.org/10.13164/re.2016.0338.

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26

Beghi, Alessandro, Marco Liberati, Sergio Mezzalira, and Stivi Peron. "Grey-box modeling of a motorcycle shock absorber for virtual prototyping applications." Simulation Modelling Practice and Theory 15, no. 8 (2007): 894–907. http://dx.doi.org/10.1016/j.simpat.2007.04.011.

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27

Halmschlager, V., M. Koller, F. Birkelbach, and R. Hofmann. "Grey Box Modeling of a Packed-Bed Regenerator Using Recurrent Neural Networks." IFAC-PapersOnLine 52, no. 16 (2019): 765–70. http://dx.doi.org/10.1016/j.ifacol.2019.12.055.

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28

Liu, Xin-yu, Yi-ping Li, Ya-xing Wang, and Xi-sheng Feng. "Hydrodynamic modeling with grey-box method of a foil-like underwater vehicle." China Ocean Engineering 31, no. 6 (2017): 773–80. http://dx.doi.org/10.1007/s13344-017-0088-0.

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29

Tan, Xin, Hideyuki Tanaka, and Yoshito Ohta. "Grey-box Modeling of Rotary Type Pendulum System with Position-Variable Load*." IFAC Proceedings Volumes 45, no. 16 (2012): 1263–68. http://dx.doi.org/10.3182/20120711-3-be-2027.00275.

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30

Arora, Yashika, Pushpinder Walia, Mitsuhiro Hayashibe, et al. "Grey-box modeling and hypothesis testing of functional near-infrared spectroscopy-based cerebrovascular reactivity to anodal high-definition tDCS in healthy humans." PLOS Computational Biology 17, no. 10 (2021): e1009386. http://dx.doi.org/10.1371/journal.pcbi.1009386.

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Transcranial direct current stimulation (tDCS) has been shown to evoke hemodynamics response; however, the mechanisms have not been investigated systematically using systems biology approaches. Our study presents a grey-box linear model that was developed from a physiologically detailed multi-compartmental neurovascular unit model consisting of the vascular smooth muscle, perivascular space, synaptic space, and astrocyte glial cell. Then, model linearization was performed on the physiologically detailed nonlinear model to find appropriate complexity (Akaike information criterion) to fit functi

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31

Wang, Xiaopeng, Qiang Wei, Zhenwen Guo, Jialu Li, Zesheng Yang, and Wu Deng. "Assessment of Energy flexibility based on different control methods on thermally activated system: a case study on zero-energy office building." Journal of Physics: Conference Series 3001, no. 1 (2025): 012022. https://doi.org/10.1088/1742-6596/3001/1/012022.

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Abstract Thermally Activated Building Systems (TABS) integrated with high-performance building envelopes offer significant potential for energy flexibility through demand response. However, such thermal inertia poses control challenges for cooling control. Model Predictive Control (MPC) has emerged as a promising solution, but the impact of different modeling mechanisms on control performance in the context of flexibility needs to be investigated. This study compares two adaptive MPC strategies based on grey-box and black-box models against conventional rule-based control (RBC) for a thermally

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32

Lindblom, E., H. Madsen, and P. S. Mikkelsen. "Comparative uncertainty analysis of copper loads in stormwater systems using GLUE and grey-box modeling." Water Science and Technology 56, no. 6 (2007): 11–18. http://dx.doi.org/10.2166/wst.2007.585.

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In this paper two attempts to assess the uncertainty involved with model predictions of copper loads from stormwater systems are made. In the first attempt, the GLUE methodology is applied to derive model parameter sets that result in model outputs encompassing a significant number of the measurements. In the second attempt the conceptual model is reformulated to a grey-box model followed by parameter estimation. Given data from an extensive measurement campaign, the two methods suggest that the output of the stormwater pollution model is associated with significant uncertainty. With the propo

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33

Pitarch, José, Antonio Sala, and César de Prada. "A Systematic Grey-Box Modeling Methodology via Data Reconciliation and SOS Constrained Regression." Processes 7, no. 3 (2019): 170. http://dx.doi.org/10.3390/pr7030170.

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Developing the so-called grey box or hybrid models of limited complexity for process systems is the cornerstone in advanced control and real-time optimization routines. These models must be based on fundamental principles and customized with sub-models obtained from process experimental data. This allows the engineer to transfer the available process knowledge into a model. However, there is still a lack of a flexible but systematic methodology for grey-box modeling which ensures certain coherence of the experimental sub-models with the process physics. This paper proposes such a methodology b

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34

Xue, Peng, Zhengtao Ai, Dongjin Cui, and Wei Wang. "A Grey Box Modeling Method for Fast Predicting Buoyancy-Driven Natural Ventilation Rates through Multi-Opening Atriums." Sustainability 11, no. 12 (2019): 3239. http://dx.doi.org/10.3390/su11123239.

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The utilization of buoyancy-driven natural ventilation in atrium buildings during transitional seasons helps create a healthy and comfortable indoor environment by bringing fresh air indoors. Among other factors, the air flow rate is a key parameter determining the ventilation performance of an atrium. In this study, a grey box modeling method is proposed and a prediction model is built for calculating the buoyancy-driven ventilation rate using three openings. This model developed from Bruce’s neutral height-based formulation and conservation laws is supported with a theoretical structure and

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35

Tanaka, Hideyuki, and Yoshito Ohta. "Grey-box Modeling of an Inverted Pendulum System Based on PD-LTI System." Transactions of the Institute of Systems, Control and Information Engineers 29, no. 12 (2016): 544–51. http://dx.doi.org/10.5687/iscie.29.544.

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36

Oaki, Junji, and Shuichi Adachi. "Grey-box Modeling of Elastic-joint Robot with Harmonic Drive and Timing Belt." IFAC Proceedings Volumes 45, no. 16 (2012): 1401–6. http://dx.doi.org/10.3182/20120711-3-be-2027.00168.

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37

Wang, Zhi-Ao, Zhong Luo, Yun-Peng Zhu, Guang-Ze Zhou, and Bing Yu. "Grey-Box Modeling Method for Single Degree of Freedom Nonlinear Vibration Isolation System." Journal of Vibration Testing and System Dynamics 9, no. 1 (2025): 47–61. https://doi.org/10.5890/jvtsd.2025.03.003.

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38

Zhang, Mengwei, Decai Li, Junfeng Xiong, and Yuqing He. "GBM-ILM: Grey-Box Modeling Based on Incremental Learning and Mechanism for Unmanned Surface Vehicles." Journal of Marine Science and Engineering 12, no. 4 (2024): 627. http://dx.doi.org/10.3390/jmse12040627.

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Unmanned surface vehicles (USVs) have garnered significant attention across various application fields. A sufficiently accurate kinetic model is essential for achieving high-performance navigation and control of USVs. However, time-varying unobservable internal states and external disturbances pose challenges in accurately modeling the USV’s kinetics, and existing methods face difficulties in accurately estimating unknown time-varying disturbances online while ensuring precise mechanism modeling. To address this issue, a novel grey-box modeling method based on incremental learning and mechanis

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Dong, James, and Ward Whitt. "Stochastic grey-box modeling of queueing systems: fitting birth-and-death processes to data." Queueing Systems 79, no. 3-4 (2014): 391–426. http://dx.doi.org/10.1007/s11134-014-9429-3.

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40

Farooq, Abdul Atisam, Abdul Afram, Nicola Schulz, and Farrokh Janabi-Sharifi. "Grey-box modeling of a low pressure electric boiler for domestic hot water system." Applied Thermal Engineering 84 (June 2015): 257–67. http://dx.doi.org/10.1016/j.applthermaleng.2015.03.050.

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41

Jorgensen, S. Bay, and K. M. Hangos. "Grey-Box Modeling for Identification and Control: An Emerging Discipline or an Established Technology?" IFAC Proceedings Volumes 27, no. 8 (1994): 1193–98. http://dx.doi.org/10.1016/s1474-6670(17)47871-2.

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42

Bechmann, Henrik, Henrik Madsen, Niels Kj�lstad Poulsen, and Marinus K. Nielsen. "Grey box modeling of first flush and incoming wastewater at a wastewater treatment plant." Environmetrics 11, no. 1 (2000): 1–12. http://dx.doi.org/10.1002/(sici)1099-095x(200001/02)11:1<1::aid-env377>3.0.co;2-n.

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Qi, Chenkun, Feng Gao, Xianchao Zhao, and Yi Yue. "A Grey-Box Distributed Parameter Modeling Approach for a Flexible Manipulator with Nonlinear Dynamics." IFAC-PapersOnLine 48, no. 28 (2015): 544–49. http://dx.doi.org/10.1016/j.ifacol.2015.12.185.

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Brandes, M., H. Cai, J. Vivian, L. Croci, P. Heer, and R. Smith. "Data-driven modeling of heat pumps and thermal storage units for MPC." Journal of Physics: Conference Series 2600, no. 3 (2023): 032008. http://dx.doi.org/10.1088/1742-6596/2600/3/032008.

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Abstract Heat pumps can play a crucial role in the European energy strategy 2050, which aims to achieve net-zero greenhouse gas emissions. When coupled with thermal energy storage and integrated with advanced control strategies, heat pump operation can be optimized to reduce carbon footprint and respond to the needs of system operators. However, to scale in a multitude of buildings, the transferability of the modeling into heterogeneous systems is crucial. In this paper, two different interpretable linear models, a hybrid (grey-box) and a fully data-driven (black-box) model are investigated. S

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45

Tanaka, Hideyuki, and Yoshito Ohta. "Grey-box modeling of an inverted pendulum system via identification of a PD-LTI system." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2016 (2016): 112–17. http://dx.doi.org/10.5687/sss.2016.112.

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46

Garrido, Juan, Sergio Garrido-Jurado, and Francisco Vázquez. "Grey-Box Modeling and Decoupling Control of a Lab Setup of the Quadruple-Tank System." Actuators 13, no. 3 (2024): 87. http://dx.doi.org/10.3390/act13030087.

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The quadruple-tank system (QTS) is a popular educational resource in universities for studying multivariable control systems. It enables the analysis of the interaction between variables and the limitations imposed by multivariable non-minimum phase zeros, as well as the evaluation of new multivariable control methodologies. The works utilizing this system present a theoretical model that may be too idealistic and based on erroneous assumptions in real-world implementations, such as the linear behavior of the actuators. In other cases, an identified linear model is directly provided. This stud

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47

Garrido, Juan, Sergio Garrido-Jurado, and Francisco Vázquez. "Grey-Box Modeling and Decoupling Control of a Lab Setup of the Quadruple-Tank System." Actuators 13, no. 3 (2024): 87. https://doi.org/10.3390/act13030087.

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The quadruple-tank system (QTS) is a popular educational resource in universities for studying multivariable control systems. It enables the analysis of the interaction between variables and the limitations imposed by multivariable non-minimum phase zeros, as well as the evaluation of new multivariable control methodologies. The works utilizing this system present a theoretical model that may be too idealistic and based on erroneous assumptions in real-world implementations, such as the linear behavior of the actuators. In other cases, an identified linear model is directly provided. This stud

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48

Wang, Ruwen, Yu Chen, Siyu Tong, Congzhi Cheng, and Yong Kang. "Compensated Neural Network Training Algorithm with Minimized Training Dataset for Modeling the Switching Transients of SiC MOSFETs." Energies 17, no. 23 (2024): 6061. https://doi.org/10.3390/en17236061.

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Accurate modeling of the switching transients of SiC MOSFETs is essential for overvoltage evaluation, EMI prediction, and other critical applications. Due to the fast switching speed, the switching transients of SiC MOSFETs are highly sensitive to parasitic parameters and nonlinear components, making precise modeling challenging. This paper proposes a hybrid model for SiC MOSFET, in which the analytical model is treated as the basis to provide the fundamental waveforms (knowledge-driven), while the neural network (NN) is utilized to fit the high-order and nonlinear features (data-driven). An N

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Barrios, José Ángel, Gerardo Maximiliano Méndez, and Alberto Cavazos. "Hybrid-Learning Type-2 Takagi–Sugeno–Kang Fuzzy Systems for Temperature Estimation in Hot-Rolling." Metals 10, no. 6 (2020): 758. http://dx.doi.org/10.3390/met10060758.

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Entry temperature estimation is a major concern for finishing mill set-up in hot strip mills. Variations in the incoming bar conditions, frequent product changes and measurement uncertainties may cause erroneous estimation, and hence, an incorrect mill set-up causing a faulty bar head-end. In earlier works, several varieties of neuro-fuzzy systems have been tested due to their adaptation capabilities. In order to test the combination of the simplicity offered by Takagi–Sugeno–Kang systems (also known as Sugeno systems) and the modeling power of type-2 fuzzy, in this work, hybrid-learning type-

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Jiang, Mian, Xuejun Li, and Tiandong Peng. "A Grey-box Modeling Approach for the Reduction of Spatially Distributed Processes Using New Basis Functions." Information Technology Journal 12, no. 22 (2013): 7019–23. http://dx.doi.org/10.3923/itj.2013.7019.7023.

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