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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: 12 червня 2025

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Статті в журналах з теми "Grey-box modeling"

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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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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 previous study. The evaluation was carried out using goodness-of-fit statistics and extreme flow analysis using WETSPRO (Water Engineering Time Series Processing tool). Nash-Sutcliffe efficiencies (NSE) of the grey-box models were from 0.39 to 0.77 in the calibration period and from 0.30 to 0.61 in the validation period. However, the white-box model performed better in terms of NSE but has a higher bias. The best grey-box model performed better in simulating extreme flow, whereas the white-box (SWAT) model adequately simulated daily flows. Asian Australas. J. Biosci. Biotechnol. 2021, 6 (1), 26-39

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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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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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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 their number from Equations (3) to (5) and parameters (3 to 6) to be fitted, are proposed. The three models’ results are analyzed and compared to existing models of the regenerator, a data-driven and a purely physical model. The results show that all developed grey-box models can extrapolate and approximate the physical behavior of the regenerator well. In particular, the extended model II shows excellent performance. While the existing data-driven model lacks robustness and the purely physical model lacks accuracy, the hybrid grey-box models do not show significant disadvantages. Compared to the data-driven and physical model, the grey-box models especially stands out due to their high accuracy, low computational effort, and high robustness.

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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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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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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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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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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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Дисертації з теми "Grey-box modeling"

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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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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. The unknown parameters of this system that are estimated using the least squares method are the three different gap heights of the system as well as the two different eccentricities in the system. The model contains the physical properties of the system, stated in equations for the leakage in the relevant lubrication interfaces, but no relational properties for the dynamics and affects between the individual lubricating interfaces. The model developed is due to the model generality together with the data quality accessible not able to model the system with reliable quality. The model is however able to capture the general trend of the leakage in the system over the applied time series datasets.<br>Den här rapporten presenterar arbetsgången och resultatet av examensarbetet för en civilingenjörsexamen i Maskinteknik från Luleå tekniska universitet utförd av Niklas Ydebäck. Målet med examensarbetet är att utvärdera och undersöka om det är möjligt, med generella och vedertagna principer av fluidflöde mellan smorda komponenter tillsammans med de geometriska begränsningarna som hör dem till och några få kanaler av data, att modellera läckaget för en radialkolvsmotor. Modellen är en grålådemodell som med hjälp av numeriska och statistiska metoder och kända fysikaliska principer av ett system bildar en modell av systemet. De okända parametrarna av systemet som estimeras med hjälp av minsta kvadrat metoden är de tre olika typerna av spalthöjderna och de två olika eccentricitetstyperna som finns i systemets smorda kontakter. Modellen består av de fysikaliska egenskaperna i systemet, formerade i ekvationer för läckaget i de relevanta smorda kontakterna, men inga relationella egenskaper för dynamiken och effekterna mellan de olika smorda kontakterna. Den utvecklade modellen är på grund av den generella karaktären av modellen tillsammans med kvaliteten på den data som finns tillgänglig inte möjlig att modellera läckaget i systemet med tillräcklig noggrannhet. Modellen är trots detta kapabel att fånga de generella trender som återfinns i den uppmätta datan på läckaget för de applicerade dataseten.

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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 for the air mass-flow through the compressor. First linear black-box models were developed without any knowledge of the physics behind. The collected data was preprocessed to work in the modeling procedure and then models with one or more inputs where built according to the ARX model structure. To further improve the models performance, non-linear regressors was developed from physical relations for the air mass-flow and used to form grey-box models of the air mass-flow.In conclusion, the performance was evaluated through comparing the estimated air mass-flow from the best model with the estimate that an extended Kalman filter together with a physical model produced.</p><br><p>Hårdare utsläppskrav för tunga lastbilar i kombination med kundernas efterfrågan på låg bränsleförbrukning har resulterat i en intensiv utveckling av motorer och deras kontrollsystem. För att kunna styra alla dessa nya lösningar är det nödvändigt att ha tillförlitliga modeller över viktiga kontrollvariabler. En av dessa är luftmassflödet som är viktig när man ska kontrollera den mängd avgaser som återcirkuleras i EGR-systemet och för att se till att kvoten mellan luft och bränsle är korrekt i motorns cylindrar. Syftet med det här examensarbetet var att använda systemidentifiering för att ta fram en modell över luftmassflödet förbi kompressorn. Först togs linjära svartboxmodeller fram utan att ta med någon kunskap om den bakomliggande fysiken. Insamlade data förbehandlades för att passa in i modelleringsproceduren och efter det skapades i enlighet med ARX-modellstrukturen modeller med en eller flera insignaler. För att ytterligare förbättra modellernas prestanda togs icke-linjära regressorer fram med hjälp av fysikaliska relationer för luftmassflödet. Dessa användes sedan för att skapa gråboxmodeller av luftmassflödet. Avslutningsvis utvärderades prestandan genom att det estimerade luftmassflödet från den bästa modellen jämfördes med det estimat som ett utökat kalmanfilter tillsammans med fysikaliska ekvationer genererade.</p>

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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 around developing a method to better estimate the engine inlet temperature and to compensate for the disturbances which a temperature sensor near the duct skin can be exposed to. A grey box model of the system was developed based on heat transfer equations between different components in the inlet, as well as predictions of temperature changes based on a temperature model of the atmosphere and thermodynamic laws. The unknown parameters of the grey box model were estimated using flight data and tuned to minimize the mean square of the prediction error. The numerical optimization of the parameters was performed using the Matlab implementations of the BFGS and SQP algorithms. An extended Kalman filter based on the model was also implemented. The two models were then evaluated in terms of how much the mean squared error was reduced compared to just using the sensor measurement to estimate the inlet air temperature. It was also analyzed how much the models reduced the prediction errors. A cross-correlation analysis was also done to see how well the model utilized the input signals. The results show that the engine inlet temperature can be estimated with good accuracy. The two models were shown to reduce the mean square of the prediction error by between 84 % and 89 % if you compare with just using the temperature sensor to estimate the temperature. The model which utilized the Kalman filtering was shown to perform slightly better than the other model. The relevance of different subcomponents of the model were investigated in order to see if the model could be simplified and maintain similar accuracy. Some investigations were also done with the relationship between different temperatures of the inlet to further understand the flow patterns of the inlet and to perhaps improve the model even more in the future.<br>En korrekt uppskattning av lufttemperaturen vid inloppet till turbofläktmotorer är väsentlig för stabil motorfunktion eftersom den direkt påverkar motorregleringen. För militära flygplan där motorn är integrerad i flygplansskrovet krävs ofta en relativt lång luftkanal för att leda luften till motorn. En sådan kanal kan påverka temperaturmätningen på grund av det värmeutbyte som sker mellan luften i kanalen och kanalväggen, speciellt då temperaturgivaren placeras nära kanalväggen eftersom den då kan påverkas av temperaturgränsskiktet nära kanalväggen. Det här examensarbetet handlade därför om att utveckla en metod för att bättre skatta temperaturen i motorinloppet och kompensera för de störningar som en temperaturgivare nära kanalväggen kan utsättas för. En fysikalisk model av systemet togs fram baserat på värmeöverföringen mellan olika komponenter i luftintagskanalen, samt ett sätt att förutse temperaturändringar baserat på en generell temperaturmodell för atmosfären och termodynamiska lagar. Många parametrar i den fysikaliska modellen av systemet var dock okända så dessa skattades baserat på flygdata. Parametrarna anpassades till modellen på ett sådant sätt att den genomsnittliga kvadraten av modellens skattningsfel minimerades. Den numeriska optimeringen av parametrarna utfördes med hjälp av Matlabs implementation av BFGS- och SQP-algoritmerna. Ett utökat kalmanfilter baserat på modellen implementerades också. De två modellerna utvärderades i termer av hur mycket de reducerade kvadraten av skattningsfelet och jämfördes med att endast använda temperaturmätningarna för att skatta temperaturen. Det undersöktes även hur mycket skattningsfelen reducerades. Korskorrelationen mellan skattningsfelet och insignalerna undersöktes även för att se om modellen hade utnyttjat insignalerna på ett bra sätt. Resultaten visar att det går att skatta temperaturen i motorinloppet med god noggrannhet. De två modellerna visade sig reducera den genomsnittliga kvadraten av skattningsfelet med mellan 84 % och 89 % om man jämför med att bara använda temperaturgivaren för att skatta temperaturen. Den modell som utnyttjade kalmanfiltrering visade sig ge något bättre resultat än den andra modellen. Olika delmodellers relevans undersöktes för att se om modellen kunde förenklas utan att modellens noggrannhet äventyrades. Några tester utfördes även för att undersöka förhållandet mellan olika temperaturer i intaget. Detta för att få en bättre förståelse för strömningen i intaget och resultatet skulle eventuellt kunna användas för att förbättra modellen ytterligare i framtiden.

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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 possible model, this work determines the one with bestexplanatory power, defining the thermal characteristics of the studied housing unit. This method allows thedetection of underperforming dwellings among constructions with high energy-efficiency standards. This investigation reflects the feasibility of employing grey-box models to predict the dynamics of heatrelated systems. Moreover, it sets the basis for new ways of employing the monitoring data of dwellings.<br>Denna forskning syftar till att utforska nya sätt att bedöma energiprestanda inom bostäder. Huvudsyftetmed detta arbete är att föreslå en värmedynamikmodell baserad på övervakningsdata för att bidra till enenergieffektiv övergång inom byggsektorn. En omfattande studie av tillgängliga matematiska och statistiska verktyg beskrivs för att bestämma enhelhetslösning, som finns i gråboxmodeller. Denna modellstrategi ger möjlighet att förstå multivariatasystem, som kan tillämpas på en hushålls värmedynamik. Genom den iterativa processen att testa varje möjlig modell bestämmer detta arbete den med bästförklarande kraft, och definierar de studerade husenhetens termiska egenskaper. Denna metod gör detmöjligt att upptäcka underpresterande bostäder bland anläggningar med hög energieffektivitetsstandard. Denna undersökning återspeglar möjligheten att använda gråboxmodeller för att förutsäga dynamiken ivärmerelaterade system. Dessutom lägger den grunden för nya sätt att använda övervakningsdata förbostäder.

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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 presented in the context of modelling the dynamics of a chemical reaction in a fluid. Lastly, the grey-box modelling method is evaluated on a one-dimensional and two-dimensional instance of the reaction system. The results indicate that the grey-box model was able to accurately capture the dynamics of the reaction system and identify the underlying reaction.<br>Hybridmodeller konstrueras genom att kombinera modellkomponenter som härleds från grundläggande principer med modelkomponenter som bestäms empiriskt från data. I den här uppsatsen presenteras en metod för att beskriva distribuerade parametersystem genom hybridmodellering. Metoden kombinerar partiella differentialekvationer med ett neuronnätverk för att inkorporera tidigare känd kunskap om systemet samt identifiera okänd dynamik från data. Neuronnätverket tränas genom en gradientbaserad optimeringsmetod som använder sig av bakåt-läget av automatisk differentiering. För att demonstrera metoden används den för att modellera kemiska reaktioner i en fluid. Metoden appliceras slutligen på ett en-dimensionellt och ett två-dimensionellt exempel av reaktions-systemet. Resultaten indikerar att hybridmodellen lyckades återskapa beteendet hos systemet med god precision samt identifiera den underliggande reaktionen.

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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 limits. Due to the economic significance of thecurl problem, much research has been performed to find sheetdesign and processing strategies to eliminate or reducecurl.</p><p>The approach we used to tackle this problem is based ongrey-box modelling. The reasons for such an approach is thatthe physical process is very complex and nonlinear. The inuenceof some inputs is not entirely understood, and besides itdepends on a number of unknown parameters andunmodelled/unmesurable disturbances.</p><p>One of the main part of the model is based on classicallaminate theory which is used to model the dimensionalstability of multi-ply board. The main assumption is that eachlayer is considered as an homogeneous elastic medium.</p><p>The model is then complemented with a sub-model forunmodelled/umeasurable disturbances which are described asstates of a dynamical system, and estimated by means of anextended Kalman filter.</p><p>The simulated curvatures show a general agreement with themeasurements. However, the prediction errors are too large forthe model to be used in an effective way, and a bigger efforthas to be carried out in order to improve the physicalsub-models.</p><p>A chapter of this thesis discusses the modelling of thewet-end part of the paper machine with Dymola, a modelling toolfor simulation of large systems based on Modelica language.</p>

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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. The resulting model manages to simulate the major dynamics for the vertical acceleration and the angular rates well enough for future control design.

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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"

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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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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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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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5

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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6

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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7

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, 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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9

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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10

Chen, Sheng. "Complex-Valued Symmetric Radial Basis Function Network for Beamforming." In Complex-Valued Neural Networks. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-214-5.ch007.

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The complex-valued radial basis function (RBF) network proposed by Chen et al. (1994) has found many applications for processing complex-valued signals, in particular, for communication channel equalization and signal detection. This complex-valued RBF network, like many other existing RBF modeling methods, constitutes a black-box approach that seeks typically a sparse model representation extracted from the training data. Adopting black-box modeling is appropriate, if no a priori information exists regarding the underlying data generating mechanism. However, a fundamental principle in practical data modelling is that if there exists a priori information concerning the system to be modeled it should be incorporated in the modeling process. Many complex-valued signal processing problems, particularly those encountered in communication signal detection, have some inherent symmetric properties. This contribution adopts a grey-box approach to complex-valued RBF modeling and develops a complex-valued symmetric RBF (SRBF) network model. The application of this SRBF network is demonstrated using nonlinear beamforming assisted detection for multiple-antenna aided wireless systems that employ complex-valued modulation schemes. Two training algorithms for this complex-valued SRBF network are proposed. The first method is based on a modified version of the cluster-variation enhanced clustering algorithm, while the second method is derived by modifying the orthogonal-forward-selection procedure based on Fisher ratio of class separability measure. The effectiveness of the proposed complex-valued SRBF network and the efficiency of the two training algorithms are demonstrated in nonlinear beamforming application.

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Тези доповідей конференцій з теми "Grey-box modeling"

1

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). The grey-box model is trained and validated with the steady-state and transient experimental data for a large range of HCCI operating conditions. The results indicate the grey-box model significantly improves the predictions from the physical model. For 234 HCCI conditions tested, the grey-box model predicts combustion phasing, IMEP, and Texh with an average error less than 1 crank angle degree, 0.2 bar, and 6 °C respectively. The grey-box model is computationally efficient and it can be used for real-time control application of HCCI engines.

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2

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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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. For any new point prediction, the estimated residual from the black-box is combined with white-box knowledge to produce the final grey-box solution. This approach was developed for use with manufacturing processes, and applied to a powder bed fusion additive manufacturing process. It can be applied in other common modeling scenarios. Two illustrative case studies are brought into the work to test this grey-box modeling approach; first for pure mathematical rigor and second for manufacturing specifically. The results of the case studies suggest that the use of grey-box models can lower predictive errors. Moreover, the resulting black-box model that represents any residual is a usable, accurate metamodel.

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3

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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4

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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5

Bidarvatan, Mehran, Vishal Thakkar, and Mahdi Shahbakhti. "Grey-box modeling and control of HCCI engine emissions." In 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859420.

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6

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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7

Hensen, 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." In 1999 European Control Conference (ECC). IEEE, 1999. http://dx.doi.org/10.23919/ecc.1999.7099811.

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8

Bram, Mads Valentin, Leif Hansen, Dennis Severin Hansen, and Zhenyu Yang. "Extended Grey-Box Modeling of Real-Time Hydrocyclone Separation Efficiency." In 2019 18th European Control Conference (ECC). IEEE, 2019. http://dx.doi.org/10.23919/ecc.2019.8796175.

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9

Jaensch, S., T. Emmert, C. F. Silva, and W. Polifke. "A Grey-Box Identification Approach for Thermoacoustic Network Models." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-27034.

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This work discusses from a system theoretic point of view the low order modeling and identification of the acoustic scattering behavior of a ducted flame. In this context, one distinguishes between black-box and grey-box models. The former rely on time series data only and do not require any physical modeling of the system that is to be identified. The latter exploit prior knowledge of the system physics to some extent and in this sense are physically motivated. For the case of a flame stabilized in a duct, a grey-box model is formulated that comprises an acoustic part as well as sub-models for the flame dynamics and the jump conditions for acoustic variables across the region of heat release. Each of the subsystems can be modeled with or without physical a priori knowledge, in combination they yield a model for the scattering behavior of the flame. We demonstrate these concepts by analyzing a CFD model of a laminar conical premixed flame. The grey-box approach allows to optimize directly the scattering behavior of the identified model. Furthermore, we show that the method allows to estimate heat release rate fluctuations as well as the flame transfer function from acoustic measurements only.

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10

Cranmer, A., M. Shahbakhti, and J. K. Hedrick. "Grey-box modeling architectures for rotational dynamic control in automotive engines." In 2012 American Control Conference - ACC 2012. IEEE, 2012. http://dx.doi.org/10.1109/acc.2012.6314796.

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