Relevant bibliographies by topics / Parameter identification

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Parameter identification'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 August 2024

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Journal articles on the topic "Parameter identification"

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Schmidt, Ulrike, Julia Mergheim, and Paul Steinmann. "MULTISCALE PARAMETER IDENTIFICATION." International Journal for Multiscale Computational Engineering 10, no. 4 (2012): 327–42. http://dx.doi.org/10.1615/intjmultcompeng.2012002175.

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Luque, Juan C. Cutipa, Decio Crisol Donha, and Ettore Apolonio de Barros. "AUV parameter identification." IFAC Proceedings Volumes 42, no. 18 (2009): 72–77. http://dx.doi.org/10.3182/20090916-3-br-3001.0062.

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Travis, C. C., and L. W. White. "Parameter identification of distributed parameter systems." Mathematical Biosciences 77, no. 1-2 (December 1985): 341–52. http://dx.doi.org/10.1016/0025-5564(85)90105-1.

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Sagara, S., and Zhen-Yu Zhao. "Identification of System Parameters in Distributed Parameter Systems." IFAC Proceedings Volumes 23, no. 8 (August 1990): 471–76. http://dx.doi.org/10.1016/s1474-6670(17)51960-6.

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Coca, D., and S. A. Billings. "Direct parameter identification of distributed parameter systems." International Journal of Systems Science 31, no. 1 (January 2000): 11–17. http://dx.doi.org/10.1080/002077200291406.

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Wilhelm, Erik, Raffaele Bornatico, Rolf Widmer, Lennon Rodgers, and Gim Soh. "Electric Vehicle Parameter Identification." World Electric Vehicle Journal 5, no. 4 (December 28, 2012): 1090–99. http://dx.doi.org/10.3390/wevj5041090.

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Hou, M. "Parameter Identification of Sinusoids." IEEE Transactions on Automatic Control 57, no. 2 (February 2012): 467–72. http://dx.doi.org/10.1109/tac.2011.2164736.

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Calm, Remei, Miguel A. Sainz, Pau Herrero, Josep Vehi, and Joaquim Armengol. "PARAMETER IDENTIFICATION WITH QUANTIFIERS." IFAC Proceedings Volumes 39, no. 9 (2006): 707–12. http://dx.doi.org/10.3182/20060705-3-fr-2907.00121.

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Keyhani, A. "Synchronous Machine Parameter Identification." Electric Machines & Power Systems 20, no. 1 (January 1992): 45–69. http://dx.doi.org/10.1080/07313569208909568.

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Heng, Zhang. "Parameter Identification of LCIS." IFAC Proceedings Volumes 18, no. 5 (July 1985): 1585–88. http://dx.doi.org/10.1016/s1474-6670(17)60793-6.

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Dissertations / Theses on the topic "Parameter identification"

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Manchu, Sreenivasarao. "Parameter Identification for Mechanical Joints." Thesis, Blekinge Tekniska Högskola, Avdelningen för maskinteknik, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4309.

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All but the simplest physical systems contains mechanical joints. The behavior of these joints is sometimes the dominant factor in over all system behavior. The potential for occurence of microslip and macroslip normally makes the behavior of joints non-linear. Accurate modeling of joints requires a non-linear ramework. As clamping pressures are typically random ad variable, the behavior of the joints becomes random. Joint geometries are random along with other unknowns of the joints. Two different methods for measuring the energy dissipation are explained. In the experimental method, the energy dissipation of a non-linear joint is calculated from the slope of the envelope of the time response of acceleration. The simulation work is carried out by considering a smooth hysteresis model with the help of Matlab programming. Finally, the parameters are extracted for a specific non-linear system by comparing analytical and experimental results.
0736988322
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Jais, Mathias. "Parameter identification for Maxwell's equations." Thesis, Cardiff University, 2006. http://orca.cf.ac.uk/54581/.

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In this work we present a variational algorithm to determine the parameters iir(x) and er(x) in the Maxwell system VxE + k xTH = 0, V x H - kerE = 0 in a body Q from boundary measurements of electromagnetic pairs (n x En dci,n x Hn dn), n= 1,2,…, where n is the outer unit normal. We show that this inverse problem can be solved by minimizing a positive functional C7(m,c) and using a conjugate gradient scheme. Apart from implementations with global boundary, we also consider the case of partial boundary, where we have only data available on a subset T C dQ. Further do we develop uniqueness results, to show that the given data (n x En dn, n x Hn dn), n = 1,2,…, is a sufficient basis to solve the inverse problem. We investigate the uniqueness properties of the inverse problem in the case of global boundary data as well as in the case of partial boundary data. To show the effectivness and the stability of our approach we present various numerical results with noisy data. Finally we outline an alternative method, where one is only interested in recovering the support of the functions fi l 1 and er 1.
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Norris, Mark A. "Parameter identification in distributed structures." Diss., Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/71164.

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This dissertation develops two new techniques for the identification of parameters in distributed-parameter systems. The first technique identifies the physical parameter distributions such as mass, damping and stiffness. The second technique identifies the modal quantities of self-adjoint distributed-parameter systems. Distributed structures are distributed-parameter systems characterized by mass, damping and stiffness distributions. To identify the distributions, a new identification technique is introduced based on the finite element method. With this approach, the object is to identify "average" values of mass, damping and stiffness distributions over each finite element. This implies that the distributed parameters are identified only approximately, in the same way in which the finite element method approximates the behavior of a structure. It is common practice to represent the motion of a distributed parameter system by a linear combination of the associated modes of vibration. In theory, we have an infinite set of modes although, in practice we are concerned with only a finite linear combination of the modes. The modes of vibration possess certain properties which distinguish them from one another. Indeed, the modes of vibration are uncorrelated in time and orthogonal in space. The modal identification technique introduced in this dissertation uses path these spatial properties. Because both the temporal and spatial properties are used, the method does not encounter problems when the natural frequencies are closely-spaced or repeated.
Ph. D.
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Sui, Liqi. "Uncertainty management in parameter identification." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2330/document.

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Afin d'obtenir des simulations plus prédictives et plus précises du comportement mécanique des structures, des modèles matériau de plus en plus complexes ont été développés. Aujourd'hui, la caractérisation des propriétés des matériaux est donc un objectif prioritaire. Elle exige des méthodes et des tests d'identification dédiés dans des conditions les plus proches possible des cas de service. Cette thèse vise à développer une méthodologie d'identification efficace pour trouver les paramètres des propriétés matériau, en tenant compte de toutes les informations disponibles. L'information utilisée pour l'identification est à la fois théorique, expérimentale et empirique : l'information théorique est liée aux modèles mécaniques dont l'incertitude est épistémique; l'information expérimentale provient ici de la mesure de champs cinématiques obtenues pendant l'essai ct dont l'incertitude est aléatoire; l'information empirique est liée à l'information à priori associée à une incertitude épistémique ainsi. La difficulté principale est que l'information disponible n'est pas toujours fiable et que les incertitudes correspondantes sont hétérogènes. Cette difficulté est surmontée par l'utilisation de la théorie des fonctions de croyance. En offrant un cadre général pour représenter et quantifier les incertitudes hétérogènes, la performance de l'identification est améliorée. Une stratégie basée sur la théorie des fonctions de croyance est proposée pour identifier les propriétés élastiques macro et micro des matériaux multi-structures. Dans cette stratégie, les incertitudes liées aux modèles et aux mesures sont analysées et quantifiées. Cette stratégie est ensuite étendue pour prendre en compte l'information à priori et quantifier l'incertitude associée
In order to obtain more predictive and accurate simulations of mechanical behaviour in the practical environment, more and more complex material models have been developed. Nowadays, the characterization of material properties remains a top-priority objective. It requires dedicated identification methods and tests in conditions as close as possible to the real ones. This thesis aims at developing an effective identification methodology to find the material property parameters, taking advantages of all available information. The information used for the identification is theoretical, experimental, and empirical: the theoretical information is linked to the mechanical models whose uncertainty is epistemic; the experimental information consists in the full-field measurement whose uncertainty is aleatory; the empirical information is related to the prior information with epistemic uncertainty as well. The main difficulty is that the available information is not always reliable and its corresponding uncertainty is heterogeneous. This difficulty is overcome by the introduction of the theory of belief functions. By offering a general framework to represent and quantify the heterogeneous uncertainties, the performance of the identification is improved. The strategy based on the belief function is proposed to identify macro and micro elastic properties of multi-structure materials. In this strategy, model and measurement uncertainties arc analysed and quantified. This strategy is subsequently developed to take prior information into consideration and quantify its corresponding uncertainty
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Kraft, Sönke. "Parameter identification for a TGV model." Phd thesis, Ecole Centrale Paris, 2012. http://tel.archives-ouvertes.fr/tel-00731143.

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This work investigates the applicability of identification methods to the suspension parameters of a TGV multi-body model. The aim is to adjust the model to the real system by estimating the suspension parameters from measured vehicle response data. Due to the nonlinear behavior of the system the time-domain based model updating has been chosen. It requires the definition and minimization of a misfit function in the time domain describing the distance between model and measurement. The fastest convergence is obtained by the use of gradient methods requiring the calculation of the derivatives of the misfit function relative to every parameter. Since the calculation from finite differences is time consuming and less accurate the gradients are calculated from the adjoint method. The application to a simplified bogie model with known mathematical description allows the identification of its suspension parameters. The presence of local minima in the misfit function of the TGV model requires the use of global optimization methods. The simulated annealing and the genetic algorithm method give important reductions of the misfit function and improved parameter estimations. In following work this information could be used for further applications like the condition monitoring.
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Drexel, Michael V. "Modal parameter identification using mode isolation." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/17239.

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Rückert, Nadja, Robert S. Anderssen, and Bernd Hofmann. "Stable Parameter Identification Evaluation of Volatility." Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-85402.

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Using the dual Black-Scholes partial differential equation, Dupire derived an explicit formula, involving the ratio of partial derivatives of the evolving fair value of a European call option (ECO), for recovering information about its variable volatility. Because the prices, as a function of maturity and strike, are only available as discrete noisy observations, the evaluation of Dupire’s formula reduces to being an ill-posed numerical differentiation problem, complicated by the need to take the ratio of derivatives. In order to illustrate the nature of ill-posedness, a simple finite difference scheme is first used to approximate the partial derivatives. A new method is then proposed which reformulates the determination of the volatility, from the partial differential equation defining the fair value of the ECO, as a parameter identification activity. By using the weak formulation of this equation, the problem is localized to a subregion on which the volatility surface can be approximated by a constant or a constant multiplied by some known shape function which models the local shape of the volatility function. The essential regularization is achieved through the localization, the choice of the analytic weight function, and the application of integration-by-parts to the weak formulation to transfer the differentiation of the discrete data to the differentiation of the analytic weight function.
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Steele, Andrew D. "Time constrained qualitative model-based parameter identification." Thesis, Heriot-Watt University, 1996. http://hdl.handle.net/10399/735.

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Iacobucci, Marco. "Dynamic parameter identification of a collaborative robot." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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I robot collaborativi stanno guadagnando un interesse crescente nel campo della robotica. Dal momento che l'industria 4.0 richiede nuovi livelli di flessibilità e soluzioni innovative di prodotto, robot e umani recentemente hanno iniziato ad interagire e lavorare in un 'ambiente comune senza protezioni perimetrali. Questa tecnologia emergente può provvedere all'operatore supporto fisico o assistenza nello svolgere compiti pericolosi o faticosi. In questo scenario, un controllo in tempo reale dovrebbe essere il più affidabile possibile e minimizzare ogni rischio legato alla collaborazione tra uomo e robot. L'obiettivo di questa tesi è l'identificazione dei coefficienti dinamici che linearizzano il modello del robot e dei parametri dinamici (massa, posizione del centro di massa ed elementi dei tensori d'inerzia di ciascun membro), utili per simulare il comportamento del robot in ambiente CAD, per ottenere simulazioni dinamiche più realistiche e algoritmi di controllo in tempo reale più affidabili. Un approccio di identificazione dinamica è presentato per il Franka Emika Panda, un robot collaborativo a 7 gradi di libertà. Questo consiste nel suddividere l'identificazione in due fasi: una prima fase in cui si analizzano le sole configurazioni statiche del robot per ottenere un set di possibili masse e centri di massa, ed una seconda fase in cui si considera il robot in movimento ed è possibile ottenere alcuni valori degli elementi dei tensori d'inerzia. Seguendo questo approccio, è possibile ottenere una stima più precisa dei parametri di massa e di posizione dei centri di massa rispetto ad un approccio in cui l'identificazione viene compiuta in una singola fase, cosa che è stata successivamente dimostrata da alcuni test eseguiti sul robot stesso, i cui risultati sono stati confrontati con quelli ottenuti seguendo un altro approccio e quelli restituiti direttamente dalla libreria del robot.
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Alami, Mohsen. "Interval Based Parameter Identification for System Biology." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-75161.

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This master thesis studies the problem of parameter identification for system biology. Two methods have been studied. The method of interval analysis uses subpaving as a class of objects to manipulate and store inner and outer approximations of compact sets. This method works well with the model given as a system of differential equations, but has its limitations, since the analytical expression for the solution to the ODE is not always obtainable, which is needed for constructing the inclusion function. The other method, studied, is SDP-relaxation of a nonlinear and non-convex feasibility problem. This method, implemented in the toolbox bio.SDP, works with system of difference equations, obtained using the Euler discretization method. The discretization method is not exact, raising the need of bounding this discretization error. Several methods for bounding this error has been studied. The method of ∞-norm optimization, also called worst-case-∞-norm is applied on the one-step error estimation method. The methods have been illustrated solving two system biological problems and the resulting SCP have been compared.
Det här examensarbetet studerar problemet med parameteridentifiering för systembiologi. Två metoder har studerats. Metoden med intervallanalys använder union av intervallvektorer som klass av objekt för att manipulera och bilda inre och yttre approximationer av kompakta mängder. Denna metod fungerar väl för modeller givna som ett system av differentialekvationer, men har sina begränsningar, eftersom det analytiska uttrycket för lösningen till differentialekvationen som är nödvändigt att känna till för att kunna formulera inkluderande funktioner, inte alltid är tillgängliga. Den andra studerade metoden, använder SDP-relaxering, som ett sätt att komma runt problemet med olinjäritet och icke-konvexitet i systemet. Denna metod, implementerad i toolboxen bio.SDP, utgår från system av differensekvationer, framtagna via Eulers diskretiserings metod. Diskretiseringsmetoden innehåller fel och osäkerhet, vilket gör det nödvändigt att estimera en gräns för felets storlek. Några felestimeringsmetoder har studerats. Metoden med ∞-norm optimering, också kallat worst-case-∞-norm är tillämpat på ett-stegs felestimerings metoder. Metoderna har illustrerats genom att lösa två system biologiska problem och de accepterade parametermängderna, benämnt SCP, har jämförts och diskuterats.
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Books on the topic "Parameter identification"

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Lebbe, Luc C. Hydraulic Parameter Identification. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60117-0.

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Coca, D. Direct parameter identification of distributed parameter systems. Sheffield: University of Sheffield, Dept. of Automatic Control and Syste,s Engineering, 1998.

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Pieter, Eykhoff, Parks P. C, International Federation of Automatic Control., International Federation of Operational Research Societies., and IFAC/IFORS Symposium on Identifaction and System Parameter Estimation, (8th : 1988 : Beijing), eds. Identification and system parameter estimation. Oxford: Pergamon, 1990.

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Cuevas, Erik, Jorge Gálvez, and Omar Avalos. Recent Metaheuristics Algorithms for Parameter Identification. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-28917-1.

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Mróz, Zenon, and Georgios E. Stavroulakis, eds. Parameter Identification of Materials and Structures. Vienna: Springer Vienna, 2005. http://dx.doi.org/10.1007/3-211-38134-1.

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Brewer, Dennis W. Parameter identification for a robotic manipulator arm. Hampton, Va: ICASE, 1986.

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Walter, E. Identification of parametric models from experimental data. Berlin: Springer, 1997.

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Banks, H. Thomas. Numerical studies of identification in nonlinear distributed parameter systems. Hampton,Va: ICASE, 1989.

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1950-, Chase Rory L., ed. Automatic identification: An IFS executive briefing. Bedford, England: IFS Publications, 1988.

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Longman, Richard W. Variance and bias confidence criteria for ERA modal parameter identification. [New York]: American Institute of Aeronautics and Astronautics, 1988.

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Book chapters on the topic "Parameter identification"

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Hicher, Pierre-Yves, and Jian-Fu Shao. "Parameter Identification." In Constitutive Modeling of Soils and Rocks, 405–32. London, UK: ISTE, 2013. http://dx.doi.org/10.1002/9780470611081.ch11.

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Calvetti, Daniela, and Erkki Somersalo. "Parameter Identification." In Encyclopedia of Applied and Computational Mathematics, 1134–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-540-70529-1_426.

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Friedman, Avner, and David S. Ross. "Parameter Identification." In Mathematics in Industry, 176–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55755-2_18.

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Lebbe, Luc C. "Introduction." In Hydraulic Parameter Identification, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60117-0_1.

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Lebbe, Luc C. "Hydraulic Parameters." In Hydraulic Parameter Identification, 9–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60117-0_2.

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Lebbe, Luc C. "Evolution of analytical models of pumping tests and their interpretation methods." In Hydraulic Parameter Identification, 55–115. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60117-0_3.

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Lebbe, Luc C. "Numerical model of pumping tests in a layered groundwater reservoir." In Hydraulic Parameter Identification, 117–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60117-0_4.

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Lebbe, Luc C. "Further developments of pumping test model." In Hydraulic Parameter Identification, 175–228. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60117-0_5.

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Lebbe, Luc C. "Inverse model as tool for pumping test interpretation." In Hydraulic Parameter Identification, 229–301. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60117-0_6.

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Lebbe, Luc C. "Example of performance and interpretation of pumping tests." In Hydraulic Parameter Identification, 303–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60117-0_7.

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Conference papers on the topic "Parameter identification"

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Luque,, Cutipa. "AUV Parameter Identification." In Manoeuvring and Control of Marine Craft, edited by Donha, Decio, chair Pascoal, Antonio and Donha, Decio. Elsevier, 2009. http://dx.doi.org/10.3182/20090916-3-br-3001.00010.

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Abur, A., and Jun Zhu. "Identification of parameter errors." In Energy Society General Meeting. IEEE, 2010. http://dx.doi.org/10.1109/pes.2010.5589668.

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Vesely, I., P. Marcon, Z. Szabo, F. Zezulka, and O. Sajdl. "Parameter identification of PMSM." In 2016 Progress in Electromagnetic Research Symposium (PIERS). IEEE, 2016. http://dx.doi.org/10.1109/piers.2016.7735156.

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Rachidi, S., R. Channa, and A. Karama. "2D Parameter identification in a distributed parameter system." In 2011 International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2011. http://dx.doi.org/10.1109/icmcs.2011.5945732.

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Maes, Stephane. "Nonlinear techniques for parameter extraction from quasi-continuous wavelet transform with application to speech." In Substance Identification Technologies, edited by James L. Flanagan, Richard J. Mammone, Albert E. Brandenstein, Edward R. Pike, Stelios C. A. Thomopoulos, Marie-Paule Boyer, H. K. Huang, and Osman M. Ratib. SPIE, 1994. http://dx.doi.org/10.1117/12.172510.

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Zhang, H., G. Tuan, G. C. Foliente, and F. Ma. "Parameter Identification of Hysteretic Structures." In ASME 1998 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/detc98/cie-5516.

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Abstract This paper reports on the time-domain identification of parameters of a smooth hysteretic model proposed by Bouc and Wen. Two different identification methods, the Levenberg-Marquardt method and the simplex method, are employed to identify the hysteretic parameters.
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He, Hong, and Yonghong Tan. "An Optimizing Parameter-Tuning of Multi-Loop Controllers for Boiler Combustion Process." In Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.769-029.

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Ilunga, Masengo, and Ednah K. Onyari. "Infilling Maxima Annual Monthly Rainfall using Neural Networks: Effect of Scaling Parameter." In Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.799-106.

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Reips, Louise, and Martin Burger. "Parameter identification in medical imaging." In CNMAC 2017 - XXXVII Congresso Nacional de Matemática Aplicada e Computacional. SBMAC, 2018. http://dx.doi.org/10.5540/03.2018.006.01.0416.

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Khosla, Pradeep, and Takeo Kanade. "Parameter identification of robot dynamics." In 1985 24th IEEE Conference on Decision and Control. IEEE, 1985. http://dx.doi.org/10.1109/cdc.1985.268838.

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Reports on the topic "Parameter identification"

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Culioli, J., and V. Protopopescu. Parameter identification for generalized Lanchester's equations. Office of Scientific and Technical Information (OSTI), March 1990. http://dx.doi.org/10.2172/7196009.

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Lohne, Arild, Arne Stavland, Siv Marie Åsen, Olav Aursjø, and Aksel Hiorth. Recommended polymer workflow: Interpretation and parameter identification. University of Stavanger, November 2021. http://dx.doi.org/10.31265/usps.202.

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Injecting a polymer solution into a porous medium significantly increases the modeling complexity, compared to model a polymer bulk solution. Even if the polymer solution is injected at a constant rate into the porous medium, the polymers experience different flow regimes in each pore and pore throat. The main challenge is to assign a macroscopic porous media “viscosity” to the fluid which can be used in Darcy law to get the correct relationship between the injection rate and pressure drop. One can achieve this by simply tabulating experimental results (e.g., injection rate vs pressure drop). The challenge with the tabulated approach is that it requires a huge experimental database to tabulate all kind of possible situations that might occur in a reservoir (e.g., changing temperature, salinity, flooding history, permeability, porosity, wettability etc.). The approach presented in this report is to model the mechanisms and describe them in terms of mathematical models. The mathematical model contains a limited number of parameters that needs to be determined experimentally. Once these parameters are determined, there is in principle no need to perform additional experiments.
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Marco, David B., Alfredo Martins, and Anthony J. Healy. Surge Motion Parameter Identification for the NPS Phoenix AUV. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada435927.

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Gray, Genetha Anne. Parameter identification for the electrical modeling of semiconductor bridges. Office of Scientific and Technical Information (OSTI), March 2005. http://dx.doi.org/10.2172/922775.

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Gardner, William A. Exploitation of Cyclostationarity for Signal-Parameter Estimation and System Identification. Fort Belvoir, VA: Defense Technical Information Center, June 1993. http://dx.doi.org/10.21236/ada267137.

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Honoré, Bo E., and Luojia Hu. Sample Selection Models Without Exclusion Restrictions: Parameter Heterogeneity and Partial Identification. Federal Reserve Bank of Chicago, 2022. http://dx.doi.org/10.21033/wp-2022-33.

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Dennis, Jr, Williamson J. E., and Karen A. A New Parallel Optimization Algorithm for Parameter Identification in Ordinary Differential Equations. Fort Belvoir, VA: Defense Technical Information Center, September 1988. http://dx.doi.org/10.21236/ada455254.

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Farhat, Charles. Real Time Predictive Flutter Analysis and Continuous Parameter Identification of Accelerating Aircraft. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada361695.

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Farhat, Charbel. Real-Time Predictive Flutter Analysis and Continuous Parameter Identification of Accelerating Aircraft. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada387498.

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Farhat, Charbel. Real-Time Predictive Flutter Analysis and Continuous Parameter Identification of Acclerating Aircraft. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada389378.

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