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Готові списки джерел за темами / Grey-box modeling

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Добірка наукової літератури з теми "Grey-box modeling"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 25 липня 2025

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Статті в журналах з теми "Grey-box modeling"

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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7

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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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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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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Анотація:

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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Дисертації з теми "Grey-box modeling"

1

Liu, Yi. "Grey-box Identification of Distributed Parameter Systems." Doctoral thesis, Stockholm, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-220.

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Ydebäck, Niklas. "Grey Box Model of Leakage In Radial Piston Hydraulic Motors." Thesis, Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-84639.

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Анотація:

This report covers the work and results of the thesis project in Mechanical Engineering from Luleå university of technology performed by Niklas Ydebäck. The objective of the thesis project is to research if it is possible, with general principles of fluid flow between components and the corresponding geometric constraints between them and just a few channels of data, to model the leakage of a radial piston hydraulic motor. The model is of the grey box kind which makes use of both numerical and statistical methods together with known physical properties of a system in order to model the system.

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3

Törnqvist, Oskar. "Black-Box Modeling of the Air Mass-Flow Through the Compressor in A Scania Diesel Engine." Thesis, Linköping University, Department of Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-52125.

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<p>Stricter emission legislation for heavy trucks in combination with the customers demand on low fuel consumption has resulted in intensive technical development of engines and their control systems. To control all these new solutions it is desirable to have reliable models for important control variables. One of them is the air mass-flow, which is important when controlling the amount of recirculated exhaust gases in the EGR system and to make sure that the air to fuel ratio is correct in the cylinders. The purpose with this thesis was to use system identification theory to develop a model f

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4

Sandvik, Gustav. "Estimation of Engine Inlet Air Temperature in Fighter Aircraft." Thesis, Linköpings universitet, Reglerteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-149557.

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An accurate estimate of the gasturbine inlet air temperature is essential to the stability of the engine since its control depends on it. Most supersonic military aircrafts have a design with the engine integrated in the fuselage which requires a rather long inlet duct from the inlet opening to the engine face. Such duct can affect the temperature measurement because of the heat flow between the inlet air and the duct skin. This is especially true when the temperature sensor is mounted close to the duct skin, which is the case for most engines. This master thesis project therefore revolved aro

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Mayo, Nardone Pablo Sabino. "Modeling the Heat Flow Dynamics of a Houses Using Stochastic Differential Equations." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302557.

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This research aims to explore new ways of assessing energy performance within housing units. The mainobjective of this work is to propose a heat dynamics model based on monitoring data, to contribute towardsan energy-efficient transition in the building sector. An extensive study on the available mathematical and statistical tools is described in order to determine aholistic solution, found in grey-box models. This model approach offers the possibility of understandingmultivariate systems, which can be applied to a housing-unit heat flow dynamics. Through the iterative process of testing each

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6

Fargus, Richard. "Grey-box modelling of physical systems." Thesis, University of Oxford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360196.

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Barkman, Patrik. "Grey-box modelling of distributed parameter systems." Thesis, KTH, Beräkningsvetenskap och beräkningsteknik (CST), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240677.

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Grey-box models are constructed by combining model components that are derived from first principles with components that are identified empirically from data. In this thesis a grey-box modelling method for describing distributed parameter systems is presented. The method combines partial differential equations with a multi-layer perceptron network in order to incorporate prior knowledge about the system while identifying unknown dynamics from data. A gradient-based optimization scheme which relies on the reverse mode of automatic differentiation is used to train the network. The method is pre

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Bortolin, Gianantonio. "On Modelling and Estimation of Curl and Twist in Multi-Ply Paperboard." Licentiate thesis, KTH, Mathematics, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1504.

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<p>This thesis describes a grey-box model for the dimensionalstability properties (i.e. curl and twist) of the carton boardproduced at AssiDomän Frövi paper mill in Sweden.AssiDomän Frövi AB is one of Sweden major cartonboard manufacturer, and produces some 350000 ton of board peryear.</p><p>Curl is defined as the departure from a at form, and it mayseriously affect the processing of the paper. For this reason,customers impose quite restrictive limits on the allowedcurvatures of the board. So, it is becoming more and moreimportant to be able to produce a carton board with a curlwithin certain

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Bäck, Marcus. "Grey-Box Modelling of a Quadrotor Using Closed-Loop Data." Thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-123488.

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In this thesis a quadrotor is studied and a linear model is derived using grey-box estimation, a discipline in system identification where a model structure based on physical relations is used and the parameters are estimated using input-output measurements. From IMU measurements and measured PWM signals to the four motors, a direct approach using the prediction-error method is applied. To investigate the impact of the unknown controller the two-stage method, a closed-loop approach in system identification, is applied as well. The direct approach was enough for estimating the model parameters

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Bortolin, Gianantonio. "Modelling and grey-box identification of curl and twist in paperboard manufacturing." Doctoral thesis, Stockholm : Department of Mathematics, KTH, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-519.

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Книги з теми "Grey-box modeling"

1

M, Hangos K., ed. grey box modelling. Wiley, 1995.

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Частини книг з теми "Grey-box modeling"

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Bruder, Frederic, and Lars Mikelsons. "Towards Grey Box Modeling in Modelica." In Robotics and Mechatronics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30036-4_17.

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2

Lindskog, P. "Fuzzy Identification from a Grey Box Modeling Point of View." In Fuzzy Model Identification. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60767-7_1.

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3

Xu, Xianzhong, Xunwen Su, Dongni Zhang, Pengyu An, and Jian Sun. "Parameter Identification of Six-Order Synchronous Motor Model Based on Grey Box Modeling." In Green Energy and Networking. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62483-5_6.

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Macarulla, M., M. Casals, N. Forcada, and M. Gangolells. "Use of Grey-Box Modeling to Determine the Air Ventilation Flows in a Room." In Lecture Notes in Management and Industrial Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54410-2_32.

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Sohlberg, Björn. "Grey Box Modelling." In Supervision and Control for Industrial Processes. Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1558-8_2.

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Rogers, T. J., G. R. Holmes, E. J. Cross, and K. Worden. "On a Grey Box Modelling Framework for Nonlinear System Identification." In Special Topics in Structural Dynamics, Volume 6. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53841-9_15.

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Schlink, Uwe, and Marialuisa Volta. "Grey Box and Component Models to Forecast Ozone Episodes: A Comparison Study." In Urban Air Quality: Measurement, Modelling and Management. Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-010-0932-4_34.

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8

Zhang, Sikai, and Elizabeth J. Cross. "Grey-Box Modelling via Gaussian Process Mean Functions for Mechanical Systems." In Data Science in Engineering, Volume 9. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76004-5_19.

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Zhang, Sikai, and Elizabeth J. Cross. "Grey-Box Modelling via Gaussian Process Mean Functions for Mechanical Systems." In Data Science in Engineering, Volume 9. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76004-5_19.

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Zhang, Sikai, Timothy J. Rogers, and Elizabeth J. Cross. "Gaussian Process Based Grey-Box Modelling for SHM of Structures Under Fluctuating Environmental Conditions." In Lecture Notes in Civil Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64908-1_6.

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Тези доповідей конференцій з теми "Grey-box modeling"

1

Ohnuma, Takumi, Keiya Watanabe, and Shou Mano. "Grey-box Modeling for IPMSMs Considering Nonlinearity of Torque and Current." In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2024. https://doi.org/10.1109/iecon55916.2024.10905204.

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2

Pan, Zitong, Fen Tang, Quanbao Zhang, and Zhiqi Liu. "Grey Box Modeling Method of Grid-Connected Inverters Based on Parameter Sensitivity." In 2025 8th International Conference on Energy, Electrical and Power Engineering (CEEPE). IEEE, 2025. https://doi.org/10.1109/ceepe64987.2025.11034070.

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3

D’Ingillo, Rocco, Andrea D’Amico, Renato Ambrosone, et al. "Enhancing EDFAs Greybox Modeling in Optical Multiplex Sections Using Few-Shot Learning." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m1j.4.

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We combine few-shot learning and grey-box modeling for EDFAs in optical lines, training a single EDFA model on 500 spectral loads and transferring it to other EDFAs using 4-8 samples, maintaining low OSNR prediction error.

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4

Pennington, Oliver, Youping Xie, Keju Jing, and Dongda Zhang. "Fed-batch bioprocess prediction and dynamic optimization from hybrid�modelling and transfer learning." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.135658.

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Hybrid modelling utilizes advantageous aspects of both mechanistic (white box) and data-driven (black box) modelling. Combining the physical interpretability of kinetic modelling with the power of a data-driven Artificial Neural Network (ANN) yields a hybrid (grey box) model with superior accuracy when compared to a traditional mechanistic model, while requiring less data than a purely data-driven model. This study demonstrates the construction a hybrid model with transfer learning for the predictive modelling and optimization of a high-cell-density microalgal fermentation process for lutein p

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5

Carecci, Davide, Giulia Quarta, Arianna Catenacci, Gianni Ferretti, and Elena Ficara. "Modelling of agro-zootechnical anaerobic co-digestion for full-scale applications." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.193145.

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To match the growing demand for biomethane production, anaerobic digestors need an optimal and time-varying adaptation of the input diet. Dynamic co-digestion constitutes a hard challenge for the limited instrumentation and control equipment typically installed aboard full-scale plants. The development of prediction models is foreseen to support process (optimal) design and control. In this work, a rigorous framework was applied to take full-scale applicability into account while dealing with the design and training of both high-fidelity and control-oriented first-principle/grey-box models, to

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6

Bidarvatan, M., and M. Shahbakhti. "Grey-Box Modeling for HCCI Engine Control." In ASME 2013 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icef2013-19097.

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High fidelity models that balance accuracy and computation load are essential for real-time model-based control of Homogeneous Charge Compression Ignition (HCCI) engines. Grey-box modeling offers an effective technique to obtain desirable HCCI control models. In this paper, a physical HCCI engine model is combined with two feed-forward artificial neural networks models to form a serial architecture grey-box model. The resulting model can predict three major HCCI engine control outputs including combustion phasing, Indicated Mean Effective Pressure (IMEP), and exhaust gas temperature (Texh). Th

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Frederic Bruder and Lars Mikelsons. "Modia and Julia for Grey Box Modeling." In 14th Modelica Conference 2021. Linköping University Electronic Press, 2021. http://dx.doi.org/10.3384/ecp2118187.

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Yang, Zhuo, Douglas Eddy, Sundar Krishnamurty, et al. "Investigating Grey-Box Modeling for Predictive Analytics in Smart Manufacturing." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67794.

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Анотація:

This paper develops a two-stage grey-box modeling approach that combines manufacturing knowledge-based (white-box) models with statistical (black-box) metamodels to improve model reusability and predictability. A white-box model can use various types of existing knowledge such as physical theory, high fidelity simulation or empirical data to build the foundation of the general model. The residual between a white-box prediction and empirical data can be represented with a black-box model. The combination of the white-box and black-box models provides the parallel hybrid structure of a grey-box.

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Ellis, Matthew J. "Machine Learning Enhanced Grey-Box Modeling for Building Thermal Modeling." In 2021 American Control Conference (ACC). IEEE, 2021. http://dx.doi.org/10.23919/acc50511.2021.9482715.

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Bram, Mads V., Leif Hansen, Dennis S. Hansen, and Zhenyu Yang. "Grey-Box modeling of an offshore deoiling hydrocyclone system." In 2017 IEEE Conference on Control Technology and Applications (CCTA). IEEE, 2017. http://dx.doi.org/10.1109/ccta.2017.8062446.

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