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Journal articles on the topic 'Dynamic modelling'

Author: Grafiati
Published: 4 June 2021
Last updated: 22 June 2024

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1

Wehrli, P., and J. Tödtli. "Dynamic behaviour modelling." Batiment International, Building Research and Practice 14, no. 1 (January 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 (August 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 (January 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 (November 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 (May 1988): 189–90. http://dx.doi.org/10.1016/0968-0004(88)90150-8.

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6

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 (June 29, 2009): 649–61. http://dx.doi.org/10.1127/0935-1221/2009/0021-1931.

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7

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 (January 31, 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. The methodology has also proven useful for modelling of proteins and hyper-degree-of-freedom systems like ropes, chains, etc. This paper captures the evolution of the DeNOC-based dynamic modelling applied to different type of systems, and its benefits over other existing methodologies. It is shown that the DeNOC-based modelling provides deeper understanding of the dynamics of a multibody system. The power of the DeNOC-based modelling has been illustrated using several numerical examples.
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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 (March 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 in a fuzzy model-based adaptive robust controller for the tracking control of the parallel manipulator.
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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 (February 8, 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 (April 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 (July 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, Hayriye Cagnan, Jakob Heinzle, Adeel Razi, and Peter Zeidman. "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 (September 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 (November 7, 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 (December 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 (December 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 (January 1, 1986): 42–47. http://dx.doi.org/10.1088/0031-9120/21/1/314.

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24

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 (November 26, 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 nonlinear forecasting.
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25

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 (June 10, 2019): 9–17. http://dx.doi.org/10.5383/jttm.01.01.002.

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26

Alzbutas, R., and V. Janilionis. "THE SIMULATION OF DYNAMIC SYSTEMS USING COMBINED MODELLING." Mathematical Modelling and Analysis 5, no. 1 (December 15, 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 interacting piecewise linear aggregates, which can include processes described with differential equations. The above mentioned approach is used in developing software for the construction and research of the models. The modelling of the dynamic systems’ control is also analyzed and developed software for the dynamic systems’ simulation is presented. It is related to the proposed combined modelling methodology. The developed dynamical simulation system ADPRO (Automatic Differentiation PROgram) extends applicability of the system SIMAS (SIMulation of the Aggregate Systems) [2] with dynamical simulation means realized with APL2 (A Programming Language 2) and based on automatic differentiation [3]. The created model of service process and its control can be used as a base for other models of wide class complex dynamics’ systems [4], the parts of which are described with differential equations.
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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 (April 2005): 209–22. http://dx.doi.org/10.1111/j.0030-1299.2005.13616.x.

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28

Qin, Guodong, Huapeng Wu, and Aihong Ji. "Equivalent Dynamic Analysis of a Cable-Driven Snake Arm Maintainer." Applied Sciences 12, no. 15 (July 26, 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 characteristics of each joint cable. The cable traction dynamics are forcibly decoupled using force analysis and joint torque equivalent transformation. Then, the forcibly equivalent dynamic model is obtained based on traditional series robot dynamic modelling methods (Lagrangian method, etc.). To verify the correctness of the equivalent dynamics, a simple model-based controller is established. In addition, a SAM prototype is produced to collect joint angles and cable forces at different trajectories. Finally, the results of the equivalent dynamics control simulation and the prototype tests demonstrate the validity of the SAM structural design and the equivalent dynamics model.
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29

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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30

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

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31

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 scenarios. It includes 3 main aspects - how sustainable is the dynamic system, what is the level of sustainability of a dynamic system under alternative development scenarios and what additions are needed to improve the functioning of the imitation model. The research question of the paper is: what imitation model can effectively analyse and forecast a dynamic system in the case of the tourism object Cesis Palace. Sustainable development researchers offer to build on traditional principles and interlinked dimensions of sustainable development: environment, economic and social, and adapt them to the dynamic system, which is characterized by the interactions between components (Tanguay et.al., 2011; Mai, Smith, 2018). One type of study that helps explain such systems is simulation modelling, which is often used when researching the interaction of dynamic systems (Johnson, 2011). MATERIALS AND METHODS The Cesis Palace complex as a tourism site was used in the paper for an example of a dynamic system, since tourism is both a dynamic system with many interlinked components and equally important are the three dimensions of sustainability for its long-term development. During the study, multiple data acquisition methods were used: a structured interview, analysis of statistics, case study analysis and an expert interview on the created imitation model. To achieve the goal of the research, a model of a tourism sustainability imitation model was created using the STELLA dynamic system modelling environment. The model and the selection of indicators were based on the three key sustainability dimensions: economy, environment and society/culture. RESULTS The result of the work is a computer model that helps to assess the sustainability of a real-life system and its dimensions by entering data generated during the study. It concludes that the tourism object under consideration is potentially sustainable. Simulating alternative development scenarios, it can be concluded that the elements of one group of indicators can affect both the sustainability level of their own dimension, as well as the indicators of other dimensions and their sustainability level, as well the sustainability of the system overall. Significant changes in the system take place in a situation where a number of indicator groups are affected by the changes. DISCUSION AND CONCLUSIONS In order to use this model further, it would be necessary to develop an improved methodology for evaluating model indicators. To ensure a more efficient model performance and data quality, data security and integrity should be taken into account. Creation of an imitation model requires the acquisition, processing and issuing of large amounts of data that are exposed to data security and integrity risks, which may have a negative impact, not only on the functioning of the model, but also on the system itself. To significantly improve the definition, selection, value assignment, and to more accurately identify the importance of interfacing elements and to express future forecasts, the author proposes to evaluate the use of machine-learning in imitation modelling. Machine-learning algorithms are increasingly used by researchers in mechanical engineering applications.
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32

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

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33

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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34

Song, Zhuoyi, Robert W. Banks, and Guy S. Bewick. "Modelling the mechanoreceptor's dynamic behaviour." Journal of Anatomy 227, no. 2 (June 25, 2015): 243–54. http://dx.doi.org/10.1111/joa.12328.

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35

Grossmann, Siegfried, and Sabine Ranft. "Dynamic Modelling of Heart Beat." Zeitschrift für Naturforschung A 50, no. 10 (October 1, 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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36

Langrock, Roland, J. Grant C. Hopcraft, Paul G. Blackwell, Victoria Goodall, Ruth King, Mu Niu, Toby A. Patterson, Martin W. Pedersen, Anna Skarin, and Robert S. Schick. "Modelling group dynamic animal movement." Methods in Ecology and Evolution 5, no. 2 (January 24, 2014): 190–99. http://dx.doi.org/10.1111/2041-210x.12155.

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37

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

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38

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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39

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

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40

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

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41

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

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42

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

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43

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

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44

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

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45

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

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46

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

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47

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

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48

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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49

Leitch, R. R. "Modelling of complex dynamic systems." IEE Proceedings D Control Theory and Applications 134, no. 4 (1987): 245. http://dx.doi.org/10.1049/ip-d.1987.0041.

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50

Wong, D. "Further dynamic modelling with LOGO." Physics Education 22, no. 3 (May 1, 1987): 174–80. http://dx.doi.org/10.1088/0031-9120/22/3/332.

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