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Journal articles on the topic 'Dynamic modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Wehrli, P., and J. Tödtli. "Dynamic behaviour modelling." Batiment International, Building Research and Practice 14, no. 1 (1986): 51–54. http://dx.doi.org/10.1080/01823328608726717.

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2

Friston, K. J., L. Harrison, and W. Penny. "Dynamic causal modelling." NeuroImage 19, no. 4 (2003): 1273–302. http://dx.doi.org/10.1016/s1053-8119(03)00202-7.

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3

Zirka, S. E., Y. I. Moroz, P. Marketos, and A. J. Moses. "Dynamic hysteresis modelling." Physica B: Condensed Matter 343, no. 1-4 (2004): 90–95. http://dx.doi.org/10.1016/j.physb.2003.08.036.

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4

Lütkepohl, Helmut. "Nonparametric dynamic modelling." Journal of Econometrics 81, no. 1 (1997): 1–5. http://dx.doi.org/10.1016/s0304-4076(97)00029-8.

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5

Mynett, A. "Modelling dynamic behaviour." Trends in Biochemical Sciences 13, no. 5 (1988): 189–90. http://dx.doi.org/10.1016/0968-0004(88)90150-8.

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6

Saha, S. K., S. V. Shah, and P. V. Nandihal. "Evolution of the DeNOC-based dynamic modelling for multibody systems." Mechanical Sciences 4, no. 1 (2013): 1–20. http://dx.doi.org/10.5194/ms-4-1-2013.

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Abstract. Dynamic modelling of a multibody system plays very essential role in its analyses. As a result, several methods for dynamic modelling have evolved over the years that allow one to analyse multibody systems in a very efficient manner. One such method of dynamic modelling is based on the concept of the Decoupled Natural Orthogonal Complement (DeNOC) matrices. The DeNOC-based methodology for dynamics modelling, since its introduction in 1995, has been applied to a variety of multibody systems such as serial, parallel, general closed-loop, flexible, legged, cam-follower, and space robots

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7

Gerya, Taras V., David Fossati, Curdin Cantieni, and Diane Seward. "Dynamic effects of aseismic ridge subduction: numerical modelling." European Journal of Mineralogy 21, no. 3 (2009): 649–61. http://dx.doi.org/10.1127/0935-1221/2009/0021-1931.

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8

Zeinali, Meysar, and Leila Notash. "FUZZY LOGIC-BASED INVERSE DYNAMIC MODELLING OF ROBOT MANIPULATORS." Transactions of the Canadian Society for Mechanical Engineering 34, no. 1 (2010): 137–50. http://dx.doi.org/10.1139/tcsme-2010-0009.

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This paper presents the design and implementation of a systematic fuzzy modelling methodology for the inverse dynamic modelling of robot manipulators. The fuzzy logic modelling methodology is motivated in part by the difficulties encountered in the modelling of complex nonlinear uncertain systems, and by the objective of developing an efficient dynamic model for the real-time model-based control. The methodology is applied to build the fuzzy logic-based inverse dynamic model of a prototyped wire-actuated parallel manipulator with uncertain dynamics. The developed inverse dynamics has been used

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9

Verlan, A. A., and Jo Sterten. "Intelligent Object-Oriented Approach to Dynamic Energy Systems’ Modelling." Mathematical and computer modelling. Series: Technical sciences, no. 21 (November 2, 2020): 43–51. http://dx.doi.org/10.32626/2308-5916.2020-21.43-51.

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10

Šleger, V. "Potential use of program Dynamic Designer in spring modelling." Research in Agricultural Engineering 50, No. 1 (2012): 1–5. http://dx.doi.org/10.17221/4918-rae.

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Program Dynamic Designer serves for kinematic and dynamical analysis of rigid body system. Models of springs and relations between force and spring deformation can be chosen and inserted into the system. In the article there is presented <br />a model of flexibly fastened body. Result of dynamical analysis is dependence of selected point deflection on time (Figs. 5, 7, 8, 10). The results can be put to use in the design of spring-loaded parts of agricultural machines.

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11

Bunjaku, Drilon. "DYNAMIC MODELLING AND ASYMPTOTIC." Journal of Electrical Engineering and Information Technologies 1, no. 1-2 (2016): 25–35. http://dx.doi.org/10.51466/jeet161-2025b.

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12

Bunjaku, Drilon. "DYNAMIC MODELLING AND ASYMPTOTIC." Journal of Electrical Engineering and Information Technologies 1, no. 1-2 (2016): 25–35. http://dx.doi.org/10.51466/jeet161-2025b.

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13

Arundell, John, Aaron Simon Blicblau, David John Richards, and Manmohan Singh. "Dynamic hip fracture modelling." ANZIAM Journal 50 (November 6, 2008): 220. http://dx.doi.org/10.21914/anziamj.v50i0.1434.

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14

Jafarian, Amirhossein, Peter Zeidman, Rob C. Wykes, Matthew Walker, and Karl J. Friston. "Adiabatic dynamic causal modelling." NeuroImage 238 (September 2021): 118243. http://dx.doi.org/10.1016/j.neuroimage.2021.118243.

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15

Bots, Pieter W. G., and Remko C. J. Dur. "Dynamic modelling of organizations." ACM SIGPLAN OOPS Messenger 3, no. 2 (1992): 3–5. http://dx.doi.org/10.1145/130943.130944.

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16

Aalen, Odd O. "Dynamic modelling and causality." Scandinavian Actuarial Journal 1987, no. 3-4 (1987): 177–90. http://dx.doi.org/10.1080/03461238.1987.10413826.

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17

Friston, K. J., Katrin H. Preller, Chris Mathys, et al. "Dynamic causal modelling revisited." NeuroImage 199 (October 2019): 730–44. http://dx.doi.org/10.1016/j.neuroimage.2017.02.045.

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18

Daunizeau, J., S. Kiebel, and K. Friston. "Stochastic Dynamic Causal Modelling." NeuroImage 47 (July 2009): S147. http://dx.doi.org/10.1016/s1053-8119(09)71500-9.

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19

Gardner, Philippa, and Sergio Maffeis. "Modelling dynamic web data." Theoretical Computer Science 342, no. 1 (2005): 104–31. http://dx.doi.org/10.1016/j.tcs.2005.06.006.

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20

Shinohara, Kazunori, and Hiroshi Okuda. "Dynamic Innovation Diffusion Modelling." Computational Economics 35, no. 1 (2009): 51–62. http://dx.doi.org/10.1007/s10614-009-9191-5.

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21

Ramos, J. I. "Mathematics for dynamic modelling." Applied Mathematical Modelling 12, no. 6 (1988): 635. http://dx.doi.org/10.1016/0307-904x(88)90061-3.

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22

Liu, Junli, Claire S. Grieson, Alex AR Webb, and Patrick J. Hussey. "Modelling dynamic plant cells." Current Opinion in Plant Biology 13, no. 6 (2010): 744–49. http://dx.doi.org/10.1016/j.pbi.2010.10.002.

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23

Wong, Denis. "Dynamic modelling with LOGO." Physics Education 21, no. 1 (1986): 42–47. http://dx.doi.org/10.1088/0031-9120/21/1/314.

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24

Stakhiv, Petro, Bohdan Melnyk, Oksana Hoholyuk, and Stepan Trokhanyak. "Application of parallel computing technology for modelling complex dynamic objects." Computational Problems of Electrical Engineering 14, no. 1 (2024): 30–35. https://doi.org/10.23939/jcpee2024.01.030.

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The paper is devoted to the development of approaches to the application of parallel algorithms in modelling complex dynamic objects. An overview of the existing principles of computer modelling based on parallel computing procedures is given. It is proposed to describe complex dynamic objects in the form of macromodels. An algorithm for parallelising computations when constructing a nonlinear macromodel of a dynamic object with a separate linear part is described. An iterative algorithm for constructing a macromodel that describes heterogeneous dynamic characteristics of an object is formulat

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25

Alzbutas, R., and V. Janilionis. "THE SIMULATION OF DYNAMIC SYSTEMS USING COMBINED MODELLING." Mathematical Modelling and Analysis 5, no. 1 (2000): 7–17. http://dx.doi.org/10.3846/13926292.2000.9637123.

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The new approach to the problems of dynamic systems simulation is proposed. The analytical and imitation modelling of non‐linear complex dynamic systems which comprise simulation of continuous and discrete processes with constant and variable parameters, are investigated. The aggregate mathematical modelling scheme [1] and the method of control sequences for discrete systems specification and simulation are used as well as the dynamic mathematical modelling scheme for continuous process formalization and modelling. According to them the investigated systems are presented as the set of interact

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26

Munch, Stephan B., Antoine Brias, George Sugihara, and Tanya L. Rogers. "Frequently asked questions about nonlinear dynamics and empirical dynamic modelling." ICES Journal of Marine Science 77, no. 4 (2019): 1463–79. http://dx.doi.org/10.1093/icesjms/fsz209.

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Abstract Complex nonlinear dynamics are ubiquitous in marine ecology. Empirical dynamic modelling can be used to infer ecosystem dynamics and species interactions while making minimal assumptions. Although there is growing enthusiasm for applying these methods, the background required to understand them is not typically part of contemporary marine ecology curricula, leading to numerous questions and potential misunderstanding. In this study, we provide a brief overview of empirical dynamic modelling, followed by answers to the ten most frequently asked questions about nonlinear dynamics and no

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27

Snäll, T., J. Pennanen, L. Kivistö, and I. Hanski. "Modelling epiphyte metapopulation dynamics in a dynamic forest landscape." Oikos 109, no. 2 (2005): 209–22. http://dx.doi.org/10.1111/j.0030-1299.2005.13616.x.

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28

Hebenstreit, Cornelia, and Martin Fellendorf. "Dynamic, multi- and intermodal bike sharing in agent-based modelling." International Journal of Traffic and Transportation Management 1, no. 1 (2019): 9–17. http://dx.doi.org/10.5383/jttm.01.01.002.

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29

Qin, Guodong, Huapeng Wu, and Aihong Ji. "Equivalent Dynamic Analysis of a Cable-Driven Snake Arm Maintainer." Applied Sciences 12, no. 15 (2022): 7494. http://dx.doi.org/10.3390/app12157494.

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In this paper, we investigate a design method for a cable-driven snake arm maintainer (SAM) and its dynamics modelling. A SAM can provide redundant degrees of freedom and high structural stiffness, as well as high load capacity and a simplified structure ideal for various narrow and extreme working environments, such as nuclear power plants. However, their serial-parallel configuration and cable drive system make the dynamics of a SAM strongly coupled, which is not conducive to accurate control. In this paper, we propose an equivalent dynamics modelling method for the strongly coupled dynamic

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30

Sadeghipour, Maryam, Mahsa Malekmohammadi, Peyman Shariatpanahi, and Majid Ghasemianpour. "The necessity of a comprehensive response to the number and composition of oral healthcare teams in Iran using the system dynamics approach." Journal of Oral Health and Oral Epidemiology 12, no. 2 (2023): 95–97. http://dx.doi.org/10.34172/johoe.2023.16.

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Background: Considering the limitations of analytical models, it is recommended that modelling methods be used to solve complex social issues. Dynamic modelling helps policymakers to have a better understanding of system behavior. In health systems, considering the available resources and impact of decisions plays a key role in improving health. The lack of a dynamic and systematic attitude sometimes ignores the impacts, and the results are not desirable despite the cost. Considering the current situation, in this article, it is strongly recommended that a system dynamics approach be adopted t

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31

Henry. "Dynamic Modelling and Rational Expectations." Annales d'Économie et de Statistique, no. 6/7 (1987): 183. http://dx.doi.org/10.2307/20075653.

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32

Motawa, Ibrahim, and Michael Oladokun. "Dynamic modelling for sustainable dwellings." Proceedings of the Institution of Civil Engineers - Engineering Sustainability 168, no. 4 (2015): 182–90. http://dx.doi.org/10.1680/ensu.14.00051.

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33

Mihailovs, Normunds, and Sarma Cakula. "DYNAMIC SYSTEM SUSTAINABILITY SIMULATION MODELLING." SOCIETY. TECHNOLOGY. SOLUTIONS. Proceedings of the International Scientific Conference 1 (April 17, 2019): 7. http://dx.doi.org/10.35363/via.sts.2019.24.

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INTRODUCTION Assessing the sustainability of dynamic, open and complex systems with many stakeholders, interrelated components and interactions and forecasting with traditional study methods is complicated and has its limitations. Therefore, often researchers, when forecasting the sustainability of a dynamic system, rely on subjective judgment without references to assessment standards. The aim of the paper is to create an imitation model for the sustainability of a dynamic system in order to assess and forecast the sustainability of the system under alternative development scenari

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34

Taylor, R. A. J., Marc Mangel, and Colin W. Clark. "Dynamic Modelling in Behaviour Ecology." Journal of Animal Ecology 59, no. 3 (1990): 1200. http://dx.doi.org/10.2307/5050.

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35

Pradas-Velasco, Roberto, Fernando Antoñanzas-Villar, and María Puy Martínez-Zárate. "Dynamic Modelling of Infectious Diseases." PharmacoEconomics 26, no. 1 (2008): 45–56. http://dx.doi.org/10.2165/00019053-200826010-00005.

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36

Song, Zhuoyi, Robert W. Banks, and Guy S. Bewick. "Modelling the mechanoreceptor's dynamic behaviour." Journal of Anatomy 227, no. 2 (2015): 243–54. http://dx.doi.org/10.1111/joa.12328.

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37

Grossmann, Siegfried, and Sabine Ranft. "Dynamic Modelling of Heart Beat." Zeitschrift für Naturforschung A 50, no. 10 (1995): 915–20. http://dx.doi.org/10.1515/zna-1995-1002.

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Abstract Two recently introduced methods to analyze heart beat are checked by applying them to numerically produced time series representing artificial heart beat. The phase space diagram method (G. E. Morfill, G. Schmidt) depends on the variability of the sinus rhythm and the coupling interval of the extrasystoles. The risk index method (J. Kurths et al.) seems to measure different aspects of heart beat.

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38

Langrock, Roland, J. Grant C. Hopcraft, Paul G. Blackwell, et al. "Modelling group dynamic animal movement." Methods in Ecology and Evolution 5, no. 2 (2014): 190–99. http://dx.doi.org/10.1111/2041-210x.12155.

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39

SCHENKER, BENEDICT, and MUKUL AGARWAL. "Dynamic modelling using neural networks." International Journal of Systems Science 28, no. 12 (1997): 1285–98. http://dx.doi.org/10.1080/00207729708929484.

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40

Hinchliffe, Mark, and Mark Willis. "DYNAMIC MODELLING USING GENETIC PROGRAMMING." IFAC Proceedings Volumes 35, no. 1 (2002): 193–98. http://dx.doi.org/10.3182/20020721-6-es-1901.00443.

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41

Adams, Nicholas J., and Christopher K. I. Williams. "Dynamic trees for image modelling." Image and Vision Computing 21, no. 10 (2003): 865–77. http://dx.doi.org/10.1016/s0262-8856(03)00073-8.

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42

Baffi, G., E. B. Martin, and A. J. Morris. "Dynamic Non-Linear PLS Modelling." IFAC Proceedings Volumes 33, no. 10 (2000): 159–64. http://dx.doi.org/10.1016/s1474-6670(17)38535-x.

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43

Currie, C. S. M. "Bayesian methodology for dynamic modelling." Journal of Simulation 1, no. 2 (2007): 97–107. http://dx.doi.org/10.1057/palgrave.jos.4250014.

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44

Peterson, Eric L., Jonathan A. Harris, and Lal C. Wadhwa. "CFD modelling pond dynamic processes." Aquacultural Engineering 23, no. 1-3 (2000): 61–93. http://dx.doi.org/10.1016/s0144-8609(00)00050-9.

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45

Neesse, Th, and J. Dueck. "Dynamic modelling of the hydrocyclone." Minerals Engineering 20, no. 4 (2007): 380–86. http://dx.doi.org/10.1016/j.mineng.2006.11.004.

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46

Ziabicki, Andrzej, Leszek Jarecki, and Andrzej Wasiak. "Dynamic modelling of melt spinning." Computational and Theoretical Polymer Science 8, no. 1-2 (1998): 143–57. http://dx.doi.org/10.1016/s1089-3156(98)00028-2.

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47

Bishop, S. R., P. G. Holborn, A. N. Beard, and D. D. Drysdale. "Dynamic modelling of building fires." Applied Mathematical Modelling 17, no. 4 (1993): 170–83. http://dx.doi.org/10.1016/0307-904x(93)90105-p.

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48

Hazzard, J. F., and R. P. Young. "Dynamic modelling of induced seismicity." International Journal of Rock Mechanics and Mining Sciences 41, no. 8 (2004): 1365–76. http://dx.doi.org/10.1016/j.ijrmms.2004.09.005.

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49

Hendry, David F., and Jurgen A. Doornik. "MODELLING LINEAR DYNAMIC ECONOMETRIC SYSTEMS." Scottish Journal of Political Economy 41, no. 1 (1994): 1–33. http://dx.doi.org/10.1111/j.1467-9485.1994.tb01107.x.

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50

Muscă (Anghelache), G. D., and S. Năstac. "Dynamic modelling of overhead crane." IOP Conference Series: Materials Science and Engineering 916 (September 11, 2020): 012071. http://dx.doi.org/10.1088/1757-899x/916/1/012071.

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