Relevant bibliographies by topics / Parameter identification

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  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: 25 July 2025

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Journal articles on the topic "Parameter identification"

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Wang, Haoyuan, Ke Jia, Yinying Jiang, and Yang Zhang. "Parameter identification of photovoltaic current loop parameters based on parameter separation." Journal of Physics: Conference Series 2814, no. 1 (2024): 012039. http://dx.doi.org/10.1088/1742-6596/2814/1/012039.

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Abstract Accurate control parameters are the basis for analyzing the fault characteristics of PV, but due to technical confidentiality reasons, equipment manufacturers do not provide accurate parameters, so they need to be identified. This paper aims to solve the problem of poor accuracy of the integration coefficient identification of a current loop with low sensitivity by proposing a parameter identification method for a photovoltaic current loop based on parameter separation. By analyzing the current response of the fault and fault recovery stages, the scale coefficient kp and the integrati
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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 (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 (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 (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 (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 (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 (1992): 45–69. http://dx.doi.org/10.1080/07313569208909568.

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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 ener
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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 uniquenes
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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 "
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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 utilisé
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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 o
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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 diffe
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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 u
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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 pro
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Books on the topic "Parameter identification"

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Lebbe, Luc C. Hydraulic Parameter Identification. 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. 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. Pergamon, 1990.

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Cuevas, Erik, Jorge Gálvez, and Omar Avalos. Recent Metaheuristics Algorithms for Parameter Identification. 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. 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. ICASE, 1986.

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

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

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

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Longman, Richard W. Variance and bias confidence criteria for ERA modal parameter identification. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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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Hatami, Ehsan, and Martin Steinberger. "Robust Parameter Identification via Reinforcement Learning." In 2025 25th International Conference on Process Control (PC). IEEE, 2025. https://doi.org/10.1109/pc65047.2025.11047331.

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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, et al. SPIE, 1994. http://dx.doi.org/10.1117/12.172510.

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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. 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. ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.799-106.

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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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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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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), 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, 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).
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Marco, David B., Alfredo Martins, and Anthony J. Healy. Surge Motion Parameter Identification for the NPS Phoenix AUV. Defense Technical Information Center, 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), 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. Defense Technical Information Center, 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. Defense Technical Information Center, 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. Defense Technical Information Center, 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. Defense Technical Information Center, 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. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada389378.

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