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Journal articles on the topic 'Parameters Model'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Shpolianskyi, O. "ADJUSTMENT OF THE MATLAB SURGE ARRESTER MODEL PARAMETERS." Tekhnichna Elektrodynamika 2020, no. 5 (2020): 49–53. http://dx.doi.org/10.15407/techned2020.05.049.

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2

Meedeniya, Indika, Irene Moser, Aldeida Aleti, and Lars Grunske. "Evaluating probabilistic models with uncertain model parameters." Software & Systems Modeling 13, no. 4 (2012): 1395–415. http://dx.doi.org/10.1007/s10270-012-0277-5.

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3

Badiul Alam, Abul Hasan Md, Junichiro Takeuchi, and Toshihiko Kawachi. "Identification and Uncertainty Assessment of Model Parameters of Distributed Runoff Model Using Bayesian Inference." Journal of Rainwater Catchment Systems 13, no. 1 (2007): 1–10. http://dx.doi.org/10.7132/jrcsa.kj00004675580.

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4

Vajda, F., and E. D. Della Torre. "Relationship between model parameters and recording industry parameters." IEEE Transactions on Magnetics 33, no. 2 (1997): 1588–91. http://dx.doi.org/10.1109/20.582570.

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5

García Sánchez, F. J., J. J. Liou, and A. Ortiz-Conde. "Calculating double-exponential diode model parameters from previously extracted single-exponential model parameters." Electronics Letters 31, no. 1 (1995): 71–72. http://dx.doi.org/10.1049/el:19950030.

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6

Vareikyte, Aiste. "Model parameters to helping women in crisis pregnancy: Specialists’ view." New Trends and Issues Proceedings on Humanities and Social Sciences 2, no. 6 (2017): 66–74. http://dx.doi.org/10.18844/gjhss.v2i6.1432.

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7

Greenland, Sander, and George Maldonado. "The interpretation of multiplicative-model parameters as standardized parameters." Statistics in Medicine 13, no. 10 (1994): 989–99. http://dx.doi.org/10.1002/sim.4780131002.

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8

Reński, Andrzej. "Identification of Driver Model Parameters." International Journal of Occupational Safety and Ergonomics 7, no. 1 (2001): 79–92. http://dx.doi.org/10.1080/10803548.2001.11076478.

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9

Jeff B. Blau, D. A. Woolhiser, and L. J. Lane. "Identification of Erosion Model Parameters." Transactions of the ASAE 31, no. 3 (1988): 0839–45. http://dx.doi.org/10.13031/2013.30789.

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10

BARKER, ANTHONY R. "STATUS OF STANDARD MODEL PARAMETERS." International Journal of Modern Physics A 16, supp01a (2001): 25–40. http://dx.doi.org/10.1142/s0217751x01006280.

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Recent experimental progress in determining the parameters of the electroweak sector of the Standard Model is summarized. The principal topics addressed include the precision determination of electroweak parameters using LEP and SLD data at the Z0 pole, measurements of the W mass by LEP-II, CDF, and D0, and determination of the elements of the CKM matrix using a variety of experimental methods.

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11

Cibira, Gabriel, and Marcela Koščová. "Photovoltaic module parameters acquisition model." Applied Surface Science 312 (September 2014): 74–80. http://dx.doi.org/10.1016/j.apsusc.2014.05.080.

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12

Merz, Ralf, and Günter Blöschl. "Regionalisation of catchment model parameters." Journal of Hydrology 287, no. 1-4 (2004): 95–123. http://dx.doi.org/10.1016/j.jhydrol.2003.09.028.

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13

Stephenson, D. "Selection of Stormwater Model Parameters." Journal of Environmental Engineering 115, no. 1 (1989): 210–20. http://dx.doi.org/10.1061/(asce)0733-9372(1989)115:1(210).

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14

van der Linden, Wim J., and Michelle D. Barrett. "Linking Item Response Model Parameters." Psychometrika 81, no. 3 (2015): 650–73. http://dx.doi.org/10.1007/s11336-015-9469-6.

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15

Pitchaimani, M. "Uniqueness of allometry model parameters." Journal of Applied Mathematics and Computing 28, no. 1-2 (2008): 485–500. http://dx.doi.org/10.1007/s12190-008-0105-9.

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16

Lyagushyn, S. F., and A. I. Sokolovsky. "Order parameters in Dicke model." Journal of Physics and Electronics 30, no. 2 (2022): 15–22. http://dx.doi.org/10.15421/332213.

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Dicke model provides describing the superradiance effect when the spontaneous emission of a great number of excited two-level atoms is running in a correlated way due to their interaction via field. The process is identical to a giant dipole emission. Since the ordering takes place, it is interesting to study the possibility for using the terms of phase transition theory in such physical situation. Equilibrium properties of Dicke type models were studied for a long time because of their relevance for explaining some phenomena in matter-field interaction. The existence of an ordered phase in th

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17

Soheli, Farhana. "Design of carbon nanotube field effect transistor (CNTFET) small signal model." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (2020): 180–87. https://doi.org/10.11591/ijece.v10i1.pp180-187.

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The progress of Carbon Nanotube Field Effect Transistor (CNTFET) devices has facilitated the trimness of mobile phones, computers and all other electronic devices. CNTFET devices contribute to model these electronics instruments that require designing the devices. This research consists of the design and verification of the CNTFET device's small signal model. Scattering parameters (S-parameters) is extracted from the CNTFET model to construct equivalent small model circuit. Current sources, capacitors and resistors are involved to evaluate this equivalent circuit. S-parameters and small si

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18

Chapman, N. Ross, and Rui Duan. "Estimation of geoacoustic model parameters from modal amplitude information." Journal of the Acoustical Society of America 139, no. 4 (2016): 2168. http://dx.doi.org/10.1121/1.4950431.

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19

Infantolino, Benjamin W., Steph E. Forrester, Matthew T. G. Pain, and John H. Challis. "The influence of model parameters on model validation." Computer Methods in Biomechanics and Biomedical Engineering 22, no. 12 (2019): 997–1008. http://dx.doi.org/10.1080/10255842.2019.1614173.

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20

Azrag, Mohammed Adam Kunna, Tuty Asmawaty Abdul Kadir, Muhammad Nomani Kabir, and Aqeel S. Jaber. "Large-Scale Kinetic Parameters Estimation of Metabolic Model of Escherichia Coli." International Journal of Machine Learning and Computing 9, no. 2 (2019): 160–67. http://dx.doi.org/10.18178/ijmlc.2019.9.2.781.

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21

R Sukhdeve, Shitalkumar, and Manish P Kurhekar. "Parameters Estimation of HMM Model for the Classification of Biological Sequences." International Journal of Scientific Engineering and Research 4, no. 4 (2016): 21–29. https://doi.org/10.70729/ijser15739.

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22

Sengupta, P., and S. Chakraborty. "Model reduction technique for Bayesian model updating of structural parameters using simulated modal data." Proceedings of the 12th Structural Engineering Convention, SEC 2022: Themes 1-2 1, no. 1 (2022): 1403–12. http://dx.doi.org/10.38208/acp.v1.670.

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An attempt has been made to study the effectiveness of model reduction technique for Bayesian approach of model updating with incomplete modal data sets. The inverse problems in system identification require the solution of a family of plausible values of model parameters based on available data. Specifically, an iterative model reduction algorithm is proposed based on a non-linear optimization method to solve the transformation parameter such that no prior choices of response parameters are required. The modal ordinates synthesized at the unmeasured degrees of freedom (DOF) from the reduced o

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23

Kao, B. G., and T. Warholic. "BAT Model Lateral Parameters Characterization Using Finite Element Model." Tire Science and Technology 31, no. 4 (2003): 225–47. http://dx.doi.org/10.2346/1.2135270.

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Abstract The bushing analogy tire (BAT) model has been shown to be capable of modeling tire dynamics in vehicle dynamic simulations. In a recent paper, the tire and vehicle data have been used to demonstrate that the BAT model yields reasonable predictions of the vehicle vertical suspension dynamic responses up to the tire 2nd vertical mode (∼90 Hz). This paper studies the modeling of the tire dynamics in the lateral direction as the next step toward completion of the BAT model as a consistent 3-dimensional tire dynamics model. Finite element (FE) analysis is an advanced analytical method for

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24

Burr, T., and Michael S. Hamada. "Simultaneous estimation of computer model parameters and model bias." Applied Radiation and Isotopes 70, no. 8 (2012): 1675–84. http://dx.doi.org/10.1016/j.apradiso.2012.04.019.

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25

TANAKA, Ikuo, Isamu TSUJI, Hirofumi HORATA, and Kazuhide ITO. "WALL SURFACE DECOMPOSITION MODEL AND IDENTIFICATION OF MODEL PARAMETERS." Journal of Environmental Engineering (Transactions of AIJ) 79, no. 702 (2014): 671–80. http://dx.doi.org/10.3130/aije.79.671.

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26

Wu, Jonathan T. H., and Sheldon Chih-Yu Tung. "Determination of Model Parameters for the Hardening Soil Model." Transportation Infrastructure Geotechnology 7, no. 1 (2019): 55–68. http://dx.doi.org/10.1007/s40515-019-00085-8.

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27

Zaripov, S. K., R. F. Mardanov, and V. F. Sharafutdinov. "Determination of Brinkman Model Parameters Using Stokes Flow Model." Transport in Porous Media 130, no. 2 (2019): 529–57. http://dx.doi.org/10.1007/s11242-019-01324-9.

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28

Akshay, R. Naik, A. V. Deorankar Prof., and P. B. Ambhore Dr. "Design and Analysis of Deep Neural Network for Rainfall Prediction System." Advancement of Computer Technology and its Applications 3, no. 2 (2020): 1–4. https://doi.org/10.5281/zenodo.3886888.

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<em>Rainfall prediction is useful for farmers and others to take decision for doing various activities. There are various methods are available for rainfall prediction like machine learning, artificial neural network. In this proposed system we are using deep neural network for rainfall prediction, as deep neural network gives better results than machine learning algorithms. This proposed method is based on classification technique which is supervised learning method in deep neural network, this classification technique we are using for predicting rainfall. As deep neural network are capable f

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29

AlAdwani, Mohammad, and Serguei Saavedra. "Feasibility conditions of ecological models: Unfolding links between model parameters." Ecological Modelling 466 (April 2022): 109900. http://dx.doi.org/10.1016/j.ecolmodel.2022.109900.

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30

Jin, Hang, Jianhui Lin, Xieqi Chen, and Cai Yi. "Modal Parameters Identification Method Based on Symplectic Geometry Model Decomposition." Shock and Vibration 2019 (September 11, 2019): 1–26. http://dx.doi.org/10.1155/2019/5018732.

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This paper proposes a novel method of structural system modal identification, where the iterative method is introduced in symplectic geometric model decomposition (SGMD). The proposed method can decompose the measured response into finite symplectic geometric components and identify the modal parameters of time-invariant structures and the instantaneous frequency of time-variant system through each symplectic geometric component. To obtain the shape information of the structural model, the SGCs of the same frequency at different measuring points are subjected to singular value decomposition (S

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31

Zhang, Ting. "Semiparametric model building for regression models with time-varying parameters." Journal of Econometrics 187, no. 1 (2015): 189–200. http://dx.doi.org/10.1016/j.jeconom.2015.02.021.

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32

Тарасенко, С., А. Левченко та М. Пестерев. "ІДЕНТИФІКАЦІЯ МОДЕЛІ ПАРАМЕТРА ПОТОКА ВІДМОВ ОЗБРОЄННЯ ТА ВІЙСЬКОВОЇ ТЕХНІКИ ПРИ ОДНОРЕЖИМНОМУ УТРИМАННІ". Collection of scientific works of Odesa Military Academy, № 20 (14 грудня 2023): 56–62. http://dx.doi.org/10.37129/2313-7509.2023.20.56-62.

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Operation of a sample of weapons and military equipment is a set of measures that includes intended use, maintenance, repair, transportation and storage. All these components of the life cycle have their own dynamic indicators, because they are in dynamic systems. Experimental search for the best conditions for keeping weapons and military equipment samples is financially burdensome and time-consuming, and sometimes not always appropriate. The dynamic systems of operation of some samples have certain limitations in terms of the occurrence of destruction, breakdowns or even explosions. The effe

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33

Jufri, Fauzan Hanif, and Jaesung Jung. "Photovoltaic Generation Forecasting Using Artificial Neural Networks Model with Input Variables and Model Parameters Selection Algorithm in Korea." International Journal of Machine Learning and Computing 7, no. 5 (2017): 156–61. http://dx.doi.org/10.18178/ijmlc.2017.7.5.639.

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34

Sidorenko, G., and A. AlJamil. "WIND POWER PLANT PARAMETERS OPTIMIZATION MODEL." EurasianUnionScientists 5, no. 1(82) (2021): 35–41. http://dx.doi.org/10.31618/esu.2413-9335.2021.5.82.1228.

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A technical and economic model of a wind power plant has been developed. Parameterization was carried out and the main parameters of the wind power plant were identified. Statistical relationships of parameters with capital investments in wind turbines and costs of their operation and maintenance were established. The criterion for the search for optimal parameters is taken as the minimum discounted unit costs for the production of electrical energy for the economic life of the wind power plant. To do this, the electricity production in a wind power plant must be maximized by reducing the so-c

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35

Bárdossy, A., and S. K. Singh. "Robust estimation of hydrological model parameters." Hydrology and Earth System Sciences Discussions 5, no. 3 (2008): 1641–75. http://dx.doi.org/10.5194/hessd-5-1641-2008.

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Abstract. The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives an unique and very best parameter vector. The parameters of hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on pa

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36

Březina, Lukas, and Tomáš Březina. "Stewart Platform Model with Uncertain Parameters." Solid State Phenomena 164 (June 2010): 177–82. http://dx.doi.org/10.4028/www.scientific.net/ssp.164.177.

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The paper deals with development of uncertain dynamics model of a six DOF parallel mechanism (Stewart platform) suitable for H-infinity controller design. The model is based on linear state space models of the machine obtained by linearization of the SimMechanics model. The linearization is performed for two positions of the machine in its workspace. It is the nominal position and the position where each link of the machine reaches its maximal length. The uncertainties are then represented as differences between parameters of corresponding state-space matrices. The uncertain state space model

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37

Schubert, Anna-Lena, Gidon Frischkorn, Dirk Hagemann, and Andreas Voss. "Trait Characteristics of Diffusion Model Parameters." Journal of Intelligence 4, no. 3 (2016): 7. http://dx.doi.org/10.3390/jintelligence4030007.

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38

Zhang, Nianpeng, and Qiang Zhu. "Two important parameters of HPMC model." Theoretical Computer Science 905 (February 2022): 10–17. http://dx.doi.org/10.1016/j.tcs.2021.12.011.

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39

Mehrabi, Ahmad, and Mehdi Rezaei. "Cosmographic Parameters in Model-independent Approaches." Astrophysical Journal 923, no. 2 (2021): 274. http://dx.doi.org/10.3847/1538-4357/ac2fff.

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Abstract The cosmographic approach, a Taylor expansion of the Hubble function, has been used as a model-independent method to investigate the evolution of the universe in the presence of cosmological data. Apart from possible technical problems like the radius of convergence, there is an ongoing debate about the tensions that appear when one investigates some high-redshift cosmological data. In this work, we consider two common data sets, namely, Type Ia supernovae (Pantheon sample) and the Hubble data, to investigate advantages and disadvantages of the cosmographic approach. To do this, we ob

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40

Bárdossy, A., and S. K. Singh. "Robust estimation of hydrological model parameters." Hydrology and Earth System Sciences 12, no. 6 (2008): 1273–83. http://dx.doi.org/10.5194/hess-12-1273-2008.

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Abstract. The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error

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41

Pilko, A. D., and V. R. Kramar. "Model of Monetary Policy Parameters Formation." Business Inform 10, no. 501 (2019): 115–21. http://dx.doi.org/10.32983/2222-4459-2019-10-115-121.

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42

Logsdon, S. D. "Determination of Preferential Flow Model Parameters." Soil Science Society of America Journal 66, no. 4 (2002): 1095–103. http://dx.doi.org/10.2136/sssaj2002.1095.

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43

Karaali, S., S. Bilir, E. Yaz, E. Hamzaoğlu, and R. Buser. "Volume-Limited Dependent Galactic Model Parameters." Publications of the Astronomical Society of Australia 24, no. 4 (2007): 208–19. http://dx.doi.org/10.1071/as07006.

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AbstractWe estimate 34 sets of Galactic model parameters for three intermediate-latitude fields with Galactic longitudes l = 60°, l = 90°, and l = 180°, and we discussed their dependence on the volume. Also, we confirm the variation of these parameters with absolute magnitude and Galactic longitude. The star samples in two fields are restricted with bright and unit absolute-magnitude intervals, 4 < Mg ≤ 5, and 5 < Mg ≤ 6, whereas for the third field (l = 60°) a larger absolute-magnitude interval is adopted, 4 < Mg ≤ 10. The limiting apparent magnitudes of the star samples are g0 = 15

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44

ANGELSEN, B. A. J., S. AAKHUS, S. I. RABBEN, and H. G. TORP. "Model-Based Estimation of Vascular Parameters:." Echocardiography 11, no. 4 (1994): 409–23. http://dx.doi.org/10.1111/j.1540-8175.1994.tb01383.x.

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45

Chang, T. S., and M. P. Singh. "Mechanical Model Parameters for Viscoelastic Dampers." Journal of Engineering Mechanics 135, no. 6 (2009): 581–84. http://dx.doi.org/10.1061/(asce)0733-9399(2009)135:6(581).

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46

Golias, John C., and George C. Kanellaidis. "Estimation of Driver Behavior Model Parameters." Journal of Transportation Engineering 116, no. 2 (1990): 153–66. http://dx.doi.org/10.1061/(asce)0733-947x(1990)116:2(153).

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47

Griffin, Daniel W. "ESTIMATION OF PULSED SPEECH MODEL PARAMETERS." Journal of the Acoustical Society of America 132, no. 4 (2012): 2778. http://dx.doi.org/10.1121/1.4757817.

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48

Finsterle, Stefan, and Julie Najita. "Robust estimation of hydrogeologic model parameters." Water Resources Research 34, no. 11 (1998): 2939–47. http://dx.doi.org/10.1029/98wr02174.

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49

Griffioen, J. W., D. A. Barry, and J. Y. Parlange. "Interpretation of two-region model parameters." Water Resources Research 34, no. 3 (1998): 373–84. http://dx.doi.org/10.1029/97wr02027.

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50

van Oosten, J. J. M., and E. Bakker. "DETERMINATION OF MAGIC TYRE MODEL PARAMETERS." Vehicle System Dynamics 21, sup001 (1992): 19–29. http://dx.doi.org/10.1080/00423119208969995.

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