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Articoli di riviste sul tema "Parameter identification"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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

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12

Gerlach, Juergen, and Ronald Guenther. "Remarks on parameter identification." Numerische Mathematik 51, no. 1 (1987): 3–9. http://dx.doi.org/10.1007/bf01399691.

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13

Xu, Tao, Tian Long Shao, and Dong Fang Zhang. "Research on Generator Excitation Parameter Identification - PSS Low-Pass Link Parameter Identification." Advanced Materials Research 805-806 (September 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 verify

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14

Kravaris, Costas, and John H. Seinfeld. "Identification of Parameters in Distributed Parameter Systems by Regularization." SIAM Journal on Control and Optimization 23, no. 2 (1985): 217–41. http://dx.doi.org/10.1137/0323017.

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15

Spall, J. C., and J. P. Garner. "Parameter identification for state-space models with nuisance parameters." IEEE Transactions on Aerospace and Electronic Systems 26, no. 6 (1990): 992–98. http://dx.doi.org/10.1109/7.62251.

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16

Abrougui, Helmi. "Modeling and Parameter Identification of an Unmanned Ground Vehicle." International Journal of Science and Research (IJSR) 11, no. 6 (2022): 1916–21. http://dx.doi.org/10.21275/sr22619100213.

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17

Ding, Li Jie, Zhou Jing Zhang, Ying Liu, Qi Huang, and Jun Wang. "Comparsion of Two Kinds of Optimization Algorithm for Load Model Parameter Identification." Applied Mechanics and Materials 380-384 (August 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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18

Qin, Yuantian, Zhehang Yin, Quanou Yang, and Kai Zhang. "Dynamics Parameter Identification of Articulated Robot." Machines 12, no. 9 (2024): 595. http://dx.doi.org/10.3390/machines12090595.

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Dynamics parameter identification in the establishment of a multiple degree-of-freedom (DOF) robot’s dynamics model poses significant challenges. This study employs a non-symbolic numerical method to establish a dynamics model based on the Newton–Euler formula and then derives a proper dynamics model through decoupling. Initially, a minimum inertial parameter set is acquired by using QR decomposition, with the inclusion of a friction model in the robot dynamics model. Subsequently, the least squares method is employed to solve for the minimum inertial parameters, forming the basis for a compre

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19

Qin, Chuan, Yuqing Jin, Meng Tian, Ping Ju, and Shun Zhou. "Comparative Study of Global Sensitivity Analysis and Local Sensitivity Analysis in Power System Parameter Identification." Energies 16, no. 16 (2023): 5915. http://dx.doi.org/10.3390/en16165915.

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Abstract (sommario):

In the process of parameter identification, sensitivity analysis is mainly used to determine key parameters with high sensitivity in the model. Sensitivity analysis methods include local sensitivity analysis (LSA) and global sensitivity analysis (GSA). The LSA method has been widely used for power system parameter identification for a long time, while the GSA has started to be used in recent years. However, there is no clear conclusion on the impact of different sensitivity analysis methods on parameter identification results. Therefore, this paper compares and studies the roles that LSA and G

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20

HOSHI, Takeharu, Kazuya KAWAMURA, Yo KOBAYASHI, Jun OKAMOTO, and 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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21

Avcıoğlu, Sevil, Ali Türker Kutay, and Kemal Leblebicioğlu. "Identification of Physical Helicopter Models Using Subspace Identification." Journal of the American Helicopter Society 65, no. 2 (2020): 1–14. http://dx.doi.org/10.4050/jahs.65.022001.

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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 mathematica

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22

KAWASAKI, Haruhisa, and Kunitoshi NISHIMURA. "Parameter Identification of Mechanical Manipulators." Transactions of the Society of Instrument and Control Engineers 22, no. 1 (1986): 76–83. http://dx.doi.org/10.9746/sicetr1965.22.76.

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23

Torres, L., G. Besançon, and C. Verde. "Leak detection using parameter identification." IFAC Proceedings Volumes 45, no. 20 (2012): 910–15. http://dx.doi.org/10.3182/20120829-3-mx-2028.00070.

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24

Khalfallah, Ali, Hédi Bel Hadj Salah, and Abdelwaheb Dogui. "Parameter Identification and Sensitivity Analysis." International Journal of Forming Processes 8, no. 2-3 (2005): 251–70. http://dx.doi.org/10.3166/ijfp.8.251-270.

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25

Ľuboš, Magdolen, Danko Ján, Milesich Tomáš, Nemec Tomáš, Sloboda Karol, and Bucha Jozef. "Dual Mass Flywheel Parameter Identification." Strojnícky časopis - Journal of Mechanical Engineering 71, no. 2 (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 i

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26

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

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27

Tadi, M., and Herschel Rabitz. "Explicit Method for Parameter Identification." Journal of Guidance, Control, and Dynamics 20, no. 3 (1997): 486–91. http://dx.doi.org/10.2514/2.4067.

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28

BAI, E. W., and S. S. SASTRY. "Parameter identification using prior information†." International Journal of Control 44, no. 2 (1986): 455–73. http://dx.doi.org/10.1080/00207178608933612.

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29

Zhang, Yong Gui, Chen Rong Liu, and Peng Liu. "Industrial Robot Kinematics Parameter Identification." Advanced Materials Research 889-890 (February 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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30

Iwata, Makoto. "Parameter Identification and Vibration Control." Journal of the Robotics Society of Japan 13, no. 8 (1995): 1084–88. http://dx.doi.org/10.7210/jrsj.13.1084.

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31

Eder, Rafael, Christian Zehetner, and 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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32

Nawrocka, Agata, and Andrzej Kot. "Balance Platform Model Parameter Identification." Solid State Phenomena 198 (March 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 meas

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33

Knowles, Ian. "Parameter identification for elliptic problems." Journal of Computational and Applied Mathematics 131, no. 1-2 (2001): 175–94. http://dx.doi.org/10.1016/s0377-0427(00)00275-2.

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34

Jakšić, Nikola. "Power law damping parameter identification." Journal of Sound and Vibration 330, no. 24 (2011): 5878–93. http://dx.doi.org/10.1016/j.jsv.2011.07.029.

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35

Baruh, H., and L. Meirovitch. "Parameter identification in distributed systems." Journal of Sound and Vibration 101, no. 4 (1985): 551–64. http://dx.doi.org/10.1016/s0022-460x(85)80071-7.

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36

Sugisaka, M., A. Tominaga, T. Sakamoto, et al. "Parameter Identification in Solar Collectors." IFAC Proceedings Volumes 18, no. 5 (1985): 1101–5. http://dx.doi.org/10.1016/s1474-6670(17)60709-2.

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37

Zhu, J., and A. Abur. "Identification of Network Parameter Errors." IEEE Transactions on Power Systems 21, no. 2 (2006): 586–92. http://dx.doi.org/10.1109/tpwrs.2006.873419.

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38

Ohkami, T., and G. Swoboda. "Parameter identification of viscoelastic materials." Computers and Geotechnics 24, no. 4 (1999): 279–95. http://dx.doi.org/10.1016/s0266-352x(99)00011-7.

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39

Hadeler, K. P. "Parameter identification in epidemic models." Mathematical Biosciences 229, no. 2 (2011): 185–89. http://dx.doi.org/10.1016/j.mbs.2010.12.004.

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40

Buggisch, H., P. Mazilu, and H. Weber. "Parameter identification for viscoelastic materials." Rheologica Acta 27, no. 4 (1988): 363–68. http://dx.doi.org/10.1007/bf01332157.

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41

Guenther, Ronald, Robert Hudspeth, William McDougal, and J�rgen Gerlach. "Remarks on parameter identification. I." Numerische Mathematik 47, no. 3 (1985): 355–61. http://dx.doi.org/10.1007/bf01389584.

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42

Hudspeth, R. T., R. B. Guenther, K. L. Roley, and W. G. McDougal. "Parameter identification in radial flow." Advances in Water Resources 14, no. 5 (1991): 240–51. http://dx.doi.org/10.1016/0309-1708(91)90037-o.

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43

Sree Hari Rao, V., and Narri Yadaiah. "Parameter identification of dynamical systems." Chaos, Solitons & Fractals 23, no. 4 (2005): 1137–51. http://dx.doi.org/10.1016/j.chaos.2003.09.047.

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44

Valente, Robertt A. F., António Andrade-Campos, José F. Carvalho, and Paulo S. Cruz. "Parameter identification and shape optimization." Optimization and Engineering 12, no. 1-2 (2010): 129–52. http://dx.doi.org/10.1007/s11081-010-9126-y.

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45

van den Boogaard, H. F. P., M. J. J. Hoogkamer, and A. W. Heemink. "Parameter identification in particle models." Stochastic Hydrology and Hydraulics 7, no. 2 (1993): 109–30. http://dx.doi.org/10.1007/bf01581420.

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46

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

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47

Carpio, A., and M. L. Rapún. "Parameter Identification in Photothermal Imaging." Journal of Mathematical Imaging and Vision 49, no. 2 (2013): 273–88. http://dx.doi.org/10.1007/s10851-013-0459-y.

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48

Larsson, Roger, and Martin Enqvist. "Sequential Aerodynamic Model Parameter Identification." IFAC Proceedings Volumes 45, no. 16 (2012): 1413–18. http://dx.doi.org/10.3182/20120711-3-be-2027.00293.

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49

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

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50

Pfaff, Udo, Georg Bednarek, Bernd Kleuter, and Paul Steinmann. "Parameter identification for transmission housings." ATZ worldwide 110, no. 3 (2008): 46–51. http://dx.doi.org/10.1007/bf03224994.

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