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Articoli di riviste sul tema "Parameter identification"

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Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 maggio 2024

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1

Schmidt, Ulrike, Julia Mergheim e Paul Steinmann. "MULTISCALE PARAMETER IDENTIFICATION". International Journal for Multiscale Computational Engineering 10, n. 4 (2012): 327–42. http://dx.doi.org/10.1615/intjmultcompeng.2012002175.

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2

Luque, Juan C. Cutipa, Decio Crisol Donha e Ettore Apolonio de Barros. "AUV parameter identification". IFAC Proceedings Volumes 42, n. 18 (2009): 72–77. http://dx.doi.org/10.3182/20090916-3-br-3001.0062.

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3

Travis, C. C., e L. W. White. "Parameter identification of distributed parameter systems". Mathematical Biosciences 77, n. 1-2 (dicembre 1985): 341–52. http://dx.doi.org/10.1016/0025-5564(85)90105-1.

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4

Sagara, S., e Zhen-Yu Zhao. "Identification of System Parameters in Distributed Parameter Systems". IFAC Proceedings Volumes 23, n. 8 (agosto 1990): 471–76. http://dx.doi.org/10.1016/s1474-6670(17)51960-6.

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5

Coca, D., e S. A. Billings. "Direct parameter identification of distributed parameter systems". International Journal of Systems Science 31, n. 1 (gennaio 2000): 11–17. http://dx.doi.org/10.1080/002077200291406.

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6

Wilhelm, Erik, Raffaele Bornatico, Rolf Widmer, Lennon Rodgers e Gim Soh. "Electric Vehicle Parameter Identification". World Electric Vehicle Journal 5, n. 4 (28 dicembre 2012): 1090–99. http://dx.doi.org/10.3390/wevj5041090.

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7

Hou, M. "Parameter Identification of Sinusoids". IEEE Transactions on Automatic Control 57, n. 2 (febbraio 2012): 467–72. http://dx.doi.org/10.1109/tac.2011.2164736.

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8

Calm, Remei, Miguel A. Sainz, Pau Herrero, Josep Vehi e Joaquim Armengol. "PARAMETER IDENTIFICATION WITH QUANTIFIERS". IFAC Proceedings Volumes 39, n. 9 (2006): 707–12. http://dx.doi.org/10.3182/20060705-3-fr-2907.00121.

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9

Keyhani, A. "Synchronous Machine Parameter Identification". Electric Machines & Power Systems 20, n. 1 (gennaio 1992): 45–69. http://dx.doi.org/10.1080/07313569208909568.

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10

Heng, Zhang. "Parameter Identification of LCIS". IFAC Proceedings Volumes 18, n. 5 (luglio 1985): 1585–88. http://dx.doi.org/10.1016/s1474-6670(17)60793-6.

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11

Gerlach, Juergen, e Ronald Guenther. "Remarks on parameter identification". Numerische Mathematik 51, n. 1 (gennaio 1987): 3–9. http://dx.doi.org/10.1007/bf01399691.

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12

Xu, Tao, Tian Long Shao e Dong Fang Zhang. "Research on Generator Excitation Parameter Identification - PSS Low-Pass Link Parameter Identification". Advanced Materials Research 805-806 (settembre 2013): 716–20. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.716.

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Abstract (sommario):

Combined with the contents of the study-PSS low-pass link parameter identification. Least-squares method is selected. Using least-square method for PSS low-pass link mathematical model are also deduced. For the results, because of the mathematical model is solving nonlinear equations, cannot used by the Newton method directly. So we choose to use Newton iterations, with this feature, choose to use MATLAB software to solve the equation. Identification of the use of MATLAB software lags after the PSS parameters obtained recognition results compared with national standards, identifying and verifying the practicability.

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13

Kravaris, Costas, e John H. Seinfeld. "Identification of Parameters in Distributed Parameter Systems by Regularization". SIAM Journal on Control and Optimization 23, n. 2 (marzo 1985): 217–41. http://dx.doi.org/10.1137/0323017.

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14

Spall, J. C., e J. P. Garner. "Parameter identification for state-space models with nuisance parameters". IEEE Transactions on Aerospace and Electronic Systems 26, n. 6 (1990): 992–98. http://dx.doi.org/10.1109/7.62251.

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15

Ding, Li Jie, Zhou Jing Zhang, Ying Liu, Qi Huang e Jun Wang. "Comparsion of Two Kinds of Optimization Algorithm for Load Model Parameter Identification". Applied Mechanics and Materials 380-384 (agosto 2013): 1521–24. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1521.

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Parameter Identification is the key technology in measurement-based load modeling. In order to identify parameters in power system ,the differential method which is based on the multiple curves fitting and interpolated method are compared in the paper. Numerical results illustrate that the differential method can improve the accuracy for load modeling parameter identifications.

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16

HOSHI, Takeharu, Kazuya KAWAMURA, Yo KOBAYASHI, Jun OKAMOTO e Masakatsu G. FUJIE. "2A1-A25 Studies on Intraoperative Identification of Tissue Model Parameters : Identification Elastic Element Parameter". Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2006 (2006): _2A1—A25_1—_2A1—A25_2. http://dx.doi.org/10.1299/jsmermd.2006._2a1-a25_1.

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17

KAWASAKI, Haruhisa, e Kunitoshi NISHIMURA. "Parameter Identification of Mechanical Manipulators". Transactions of the Society of Instrument and Control Engineers 22, n. 1 (1986): 76–83. http://dx.doi.org/10.9746/sicetr1965.22.76.

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18

Torres, L., G. Besançon e C. Verde. "Leak detection using parameter identification". IFAC Proceedings Volumes 45, n. 20 (gennaio 2012): 910–15. http://dx.doi.org/10.3182/20120829-3-mx-2028.00070.

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19

Khalfallah, Ali, Hédi Bel Hadj Salah e Abdelwaheb Dogui. "Parameter Identification and Sensitivity Analysis". International Journal of Forming Processes 8, n. 2-3 (settembre 2005): 251–70. http://dx.doi.org/10.3166/ijfp.8.251-270.

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20

Ľuboš, Magdolen, Danko Ján, Milesich Tomáš, Nemec Tomáš, Sloboda Karol e Bucha Jozef. "Dual Mass Flywheel Parameter Identification". Strojnícky časopis - Journal of Mechanical Engineering 71, n. 2 (1 novembre 2021): 167–78. http://dx.doi.org/10.2478/scjme-2021-0027.

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Abstract Reducing emissions brings changes in the design of internal combustion engines and thus new challenges for dual-mass flywheels (DMF) in terms of Noise Vibration and Harshness (NVH). The first part of the article describes a simple model of a centrifugal pendulum. Consequently, a more complicated DMF dynamic model involves friction between the spring components. The second part of the article deal with the multibody model of DMF using a CAD model. The dynamic model consists of a torsion spring and two bodies. The model is compared with the experimental method, which is also described in the paper.

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21

Munir, Mohammad, Nasreen Kausar e Mohammad Shakil. "Parameter identification for multiperiodic functions". Technological Forecasting and Social Change 173 (dicembre 2021): 121134. http://dx.doi.org/10.1016/j.techfore.2021.121134.

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22

Tadi, M., e Herschel Rabitz. "Explicit Method for Parameter Identification". Journal of Guidance, Control, and Dynamics 20, n. 3 (maggio 1997): 486–91. http://dx.doi.org/10.2514/2.4067.

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23

BAI, E. W., e S. S. SASTRY. "Parameter identification using prior information†". International Journal of Control 44, n. 2 (agosto 1986): 455–73. http://dx.doi.org/10.1080/00207178608933612.

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24

Zhang, Yong Gui, Chen Rong Liu e Peng Liu. "Industrial Robot Kinematics Parameter Identification". Advanced Materials Research 889-890 (febbraio 2014): 1136–43. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.1136.

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For an industrial robots with unknown parameters, on the basis of preliminary measurement and data of the Cartesian and joints coordinates which are shown on the FlexPendant, the kinematic parameters is identified by using genetic algorithms and accurate kinematics modeling of the robot is established. Experimental data could prove the validity of this method.

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25

Iwata, Makoto. "Parameter Identification and Vibration Control." Journal of the Robotics Society of Japan 13, n. 8 (1995): 1084–88. http://dx.doi.org/10.7210/jrsj.13.1084.

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26

Eder, Rafael, Christian Zehetner e Wolfgang Kunze. "Comparison of Parameter Identification Techniques". MATEC Web of Conferences 70 (2016): 09007. http://dx.doi.org/10.1051/matecconf/20167009007.

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27

Nawrocka, Agata, e Andrzej Kot. "Balance Platform Model Parameter Identification". Solid State Phenomena 198 (marzo 2013): 439–44. http://dx.doi.org/10.4028/www.scientific.net/ssp.198.439.

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In the first stage of the identification process was to build a model describing the system, using the measurement data recorded on input and output system [. The model was built without the knowledge of the mechanisms that occur in the process, only based on the relationship between the collected data. As an input parameter the defined type of motion was used, next converted to digital form by the real-time computer and sent to the axes control card. While the output signal was registered as a change of encoders position. Sampling time 20 ms was adopted. For each axis separately recorded measurements (signals were with different amplitudes and frequencies of motion tape device) [. In the next step the estimation was performed for the selection suitable algorithm, and the determination the parameters of model selected in the previous step [. The last step was to verify, to check the results of identification process. By comparing the signals obtained in response to a signal given the model of a registered object actual output signal, it is possible to visually estimate the accuracy of the identification and designation of the error. The last part of the article shows the results of various tests.

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28

Knowles, Ian. "Parameter identification for elliptic problems". Journal of Computational and Applied Mathematics 131, n. 1-2 (giugno 2001): 175–94. http://dx.doi.org/10.1016/s0377-0427(00)00275-2.

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29

Jakšić, Nikola. "Power law damping parameter identification". Journal of Sound and Vibration 330, n. 24 (novembre 2011): 5878–93. http://dx.doi.org/10.1016/j.jsv.2011.07.029.

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30

Baruh, H., e L. Meirovitch. "Parameter identification in distributed systems". Journal of Sound and Vibration 101, n. 4 (agosto 1985): 551–64. http://dx.doi.org/10.1016/s0022-460x(85)80071-7.

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31

Sugisaka, M., A. Tominaga, T. Sakamoto, R. Fischl, P. Herczfeld, P. Kalata e C. Rorres. "Parameter Identification in Solar Collectors". IFAC Proceedings Volumes 18, n. 5 (luglio 1985): 1101–5. http://dx.doi.org/10.1016/s1474-6670(17)60709-2.

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32

Zhu, J., e A. Abur. "Identification of Network Parameter Errors". IEEE Transactions on Power Systems 21, n. 2 (maggio 2006): 586–92. http://dx.doi.org/10.1109/tpwrs.2006.873419.

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33

Ohkami, T., e G. Swoboda. "Parameter identification of viscoelastic materials". Computers and Geotechnics 24, n. 4 (giugno 1999): 279–95. http://dx.doi.org/10.1016/s0266-352x(99)00011-7.

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34

Hadeler, K. P. "Parameter identification in epidemic models". Mathematical Biosciences 229, n. 2 (febbraio 2011): 185–89. http://dx.doi.org/10.1016/j.mbs.2010.12.004.

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35

Buggisch, H., P. Mazilu e H. Weber. "Parameter identification for viscoelastic materials". Rheologica Acta 27, n. 4 (luglio 1988): 363–68. http://dx.doi.org/10.1007/bf01332157.

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36

Guenther, Ronald, Robert Hudspeth, William McDougal e J�rgen Gerlach. "Remarks on parameter identification. I". Numerische Mathematik 47, n. 3 (settembre 1985): 355–61. http://dx.doi.org/10.1007/bf01389584.

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37

Hudspeth, R. T., R. B. Guenther, K. L. Roley e W. G. McDougal. "Parameter identification in radial flow". Advances in Water Resources 14, n. 5 (ottobre 1991): 240–51. http://dx.doi.org/10.1016/0309-1708(91)90037-o.

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38

Sree Hari Rao, V., e Narri Yadaiah. "Parameter identification of dynamical systems". Chaos, Solitons & Fractals 23, n. 4 (febbraio 2005): 1137–51. http://dx.doi.org/10.1016/j.chaos.2003.09.047.

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39

Valente, Robertt A. F., António Andrade-Campos, José F. Carvalho e Paulo S. Cruz. "Parameter identification and shape optimization". Optimization and Engineering 12, n. 1-2 (12 novembre 2010): 129–52. http://dx.doi.org/10.1007/s11081-010-9126-y.

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40

van den Boogaard, H. F. P., M. J. J. Hoogkamer e A. W. Heemink. "Parameter identification in particle models". Stochastic Hydrology and Hydraulics 7, n. 2 (giugno 1993): 109–30. http://dx.doi.org/10.1007/bf01581420.

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41

Jansen, W., e E. U. Kriegel. "Parameter identification in chaotic systems". Annual Review in Automatic Programming 12 (gennaio 1985): 162–69. http://dx.doi.org/10.1016/0066-4138(85)90019-9.

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42

Carpio, A., e M. L. Rapún. "Parameter Identification in Photothermal Imaging". Journal of Mathematical Imaging and Vision 49, n. 2 (10 settembre 2013): 273–88. http://dx.doi.org/10.1007/s10851-013-0459-y.

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43

Larsson, Roger, e Martin Enqvist. "Sequential Aerodynamic Model Parameter Identification". IFAC Proceedings Volumes 45, n. 16 (luglio 2012): 1413–18. http://dx.doi.org/10.3182/20120711-3-be-2027.00293.

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44

Gallrein, A., J. De Cuyper, W. Dehandschutter e M. Bäcker. "Parameter identification for LMS CDTire". Vehicle System Dynamics 43, sup1 (gennaio 2005): 444–56. http://dx.doi.org/10.1080/00423110500230053.

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45

Pfaff, Udo, Georg Bednarek, Bernd Kleuter e Paul Steinmann. "Parameter identification for transmission housings". ATZ worldwide 110, n. 3 (marzo 2008): 46–51. http://dx.doi.org/10.1007/bf03224994.

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46

Martin, C., J. Miller e K. Pearce. "Parameter identification by continuation methods". Applied Mathematics and Computation 34, n. 1 (novembre 1989): 17–27. http://dx.doi.org/10.1016/0096-3003(89)90003-9.

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47

Mickleborough, N. C., e Y. L. Pi. "Modal parameter identification usingZ-transforms". International Journal for Numerical Methods in Engineering 28, n. 10 (ottobre 1989): 2307–21. http://dx.doi.org/10.1002/nme.1620281008.

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48

Furukawa, Tomonari. "Parameter identification with weightless regularization". International Journal for Numerical Methods in Engineering 52, n. 3 (30 settembre 2001): 219–38. http://dx.doi.org/10.1002/nme.215.

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49

KAWAI, Tadao, e Yusuke NAITO. "Parameter Identification of Beam Using Image Processing (5th Report, Parameter Identification Using Orthogonal Functions)". Transactions of the Japan Society of Mechanical Engineers Series C 70, n. 690 (2004): 453–58. http://dx.doi.org/10.1299/kikaic.70.453.

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50

Avcıoğlu, Sevil, Ali Türker Kutay e Kemal Leblebicioğlu. "Identification of Physical Helicopter Models Using Subspace Identification". Journal of the American Helicopter Society 65, n. 2 (1 aprile 2020): 1–14. http://dx.doi.org/10.4050/jahs.65.022001.

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Abstract (sommario):

Subspace identification is a powerful tool due to its well-understood techniques based on linear algebra (orthogonal projections and intersections of subspaces) and numerical methods like singular value decomposition. However, the state space model matrices, which are obtained from conventional subspace identification algorithms, are not necessarily associated with the physical states. This can be an important deficiency when physical parameter estimation is essential. This holds for the area of helicopter flight dynamics, where physical parameter estimation is mainly conducted for mathematical model improvement, aerodynamic parameter validation, and flight controller tuning. The main objective of this study is to obtain helicopter physical parameters from subspace identification results. To achieve this objective, the subspace identification algorithm is implemented for a multirole combat helicopter using both FLIGHTLAB simulation and real flight-test data. After obtaining state space matrices via subspace identification, constrained nonlinear optimization methodologies are utilized for extracting the physical parameters. The state space matrices are transformed into equivalent physical forms via the "sequential quadratic programming" nonlinear optimization algorithm. The required objective function is generated by summing the square of similarity transformation equations. The constraints are selected with physical insight. Many runs are conducted for randomly selected initial conditions. It can be concluded that all of the significant parameters can be obtained with a high level of accuracy for the data obtained from the linear model. This strongly supports the idea behind this study. Results for the data obtained from the nonlinear model are also evaluated to be satisfactory in the light of statistical error analysis. Results for the real flight-test data are also evaluated to be good for the helicopter modes that are properly excited in the flight tests.

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