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Books on the topic 'Process control Dynamics Mathematical models'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Volgin, L. N. Optimalʹnoe diskretnoe upravlenie dinamicheskimi sistemami. Moskva: "Nauka," Glav. red. fiziko-matematicheskoĭ lit-ry, 1986.

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2

Pearson, Ronald K. Discrete-time dynamic models. New York: Oxford University Press, 1999.

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3

Huang, Jen-Kuang. Indirect identification of linear stochastic systems with known feedback dynamics. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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4

Huang, Jen-Kuang. Indirect identification of linear stochastic systems with known feedback dynamics. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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5

Process control and identification. Boston: Academic Press, 1994.

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6

Kecman, V. State-space models of lumped and distributed systems. Berlin: Springer-Verlag, 1988.

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7

System dynamics and control. Pacific Grove: PWS Publishing Company, 1999.

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8

Denkena, Berend. Process Machine Interactions: Predicition and Manipulation of Interactions between Manufacturing Processes and Machine Tool Structures. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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9

Beyreuther, Roland. Dynamics of fibre formation and processing: Modelling and application in fibre and textile industry. Berlin: Springer, 2007.

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10

D, Gunzburger Max, ed. Flow control. New York: Springer-Verlag, 1995.

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11

Kolpakov, Vasiliy. Economic and mathematical and econometric modeling: Computer workshop. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/24417.

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The textbook presents mathematical research methods and models of economic objects and processes designed for the analysis and prediction of economic factors and develop control solutions as in the deterministic conditions, and in conditions of some uncertainty, and dynamics. Each Chapter of the book consists of a theoretical framework, discussed in detail several examples and tasks for independent work. As workbench simulation uses standard office the program Excel and Mathcad. Tutorial focused on independent performance of students individual tasks on disciplines "Economic-mathematical methods" and "Econometrics". Meets the requirements of Federal state educational standard of higher education of the last generation. The publication is intended for students and postgraduate students in economic disciplines. It can also be useful as they perform final qualifying works. The book will be useful for practitioners engaged in the analysis of the current financial and economic condition and future development of firms and businesses.

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12

V, Evstigneev I., Medova E. A, and Dempster, M. A. H. 1938-, eds. Stochastic models of control and economic dynamics. London: Academic Press, 1987.

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13

Rao, Vadrevu Sree Hari. Dynamic models and control of biological systems. Dordrecht: Springer, 2009.

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14

Kōzōbutsu no shisutemu seigyo: Control theory of structural systems. Tōkyō-to Chiyoda-ku: Morikita Shuppan, 2013.

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15

Mikleš, Ján. Process modelling, identification, and control. Bratislava: Slovak Technical University Press, 2000.

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16

Process modeling, simulation, and control for chemical engineers. 2nd ed. New York: McGraw-Hill, 1990.

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17

Pedestrian dynamics: Mathematical theory and evacuation control. Boca Raton: Taylor & Francis, 2009.

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18

Tarasov, V. A. Optimizat͡s︡ii͡a︡ proizvodstvennykh kompleksov s peremennymi parametrami. Moskva: Ėnergoatomizdat, 1985.

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19

Akunovich, S. I. Diskretnye sistemy ligicheskogo upravlenii︠a︡ tekhnologicheskikh mashin: Monografii︠a︡. Minsk: I︠u︡nipak, 2006.

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20

Tatjewski, Piotr. Hierarchiczne metody rozwiązywania niewypukłych zadań optymalizacji i sterowania stanem ustalonym złożonych procesów. Warszawa: Wydawnictwa Politechniki Warszawskiej, 1988.

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21

Raspopov, B. M. Matematicheskie modeli v zadachakh upravlenii͡a︡ tekhnologicheskimi prot͡s︡essami. Frunze: Izd-vo "Ilim", 1988.

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22

T͡Sirlin, A. M. Optimalʹnye t͡sikly i t͡siklicheskie rezhimy. Moskva: Ėnergoatomizdat, 1985.

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23

Anita̧, Sebastian. Analysis and control of age-dependent population dynamics. Dordrecht: Kluwer Academic Publishers, 2000.

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24

1944-, Feldman Richard M., ed. Mathematical foundations of population dynamics. College Station: Published for the Texas Engineering Experiment Station, Texas A&M University System, by Texas A&M University Press, 1987.

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25

Aniţa, Sebastian. Analysis and Control of Age-Dependent Population Dynamics. Dordrecht: Springer Netherlands, 2000.

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26

M, Tijskens L. M., Hertog, M. L. A. T. M., and Nicolaï B. M, eds. Food process modelling. Cambridge: Woodhead, 2001.

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27

Koenig, David M. Control and analysis of noisy processes. Englewood Cliffs, N.J: Prentice Hall, 1991.

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28

Zubov, I. V. Metody analiza dinamiki upravli︠a︡emykh sistem. Moskva: Fizmatlit, 2003.

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29

Vvedenie v dinamiku slozhnykh upravli͡a︡emykh sistem. Moskva: "Nauka," Glav. red. fiziko-matematicheskoĭ lit-ry, 1985.

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30

Dindorf, Ryszard. Wybrane zagadnienia modelowania dynamiki układów hydraulicznych. Kraków: Politechnika Krakowska im. Tadeusza Kościuszki, 1995.

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31

Simulation of industrial processes for control engineers. Oxford: Butterworth-Heinemann, 1999.

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32

Krivenko, V. A. Preobrazovanii͡a︡ optimizat͡s︡ionnykh modeleĭ v zadachakh upravlenii͡a︡ i proektirovanii͡a︡ ASU TP. Frunze: "Ilim", 1989.

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33

Samantaray, Arun K. Model-based process supervision: A bond graph approach. London: Springer, 2008.

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34

Ramirez, W. Fred. Computational methods for process simulation. 2nd ed. Oxford: Butterworths, 1997.

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35

Computational methods for process simulation. Boston: Butterworths, 1989.

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36

Zeleke, Gete. Landscape dynamics and soil erosion process modelling in the North-western Ethiopian highlands. Berne, Switzerland: University of Berne, Institute of Geography, 2000.

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37

Chow, Gregory C. Analysis and control of dynamic economic systems. Malabar, Fla: R.E. Krieger, 1986.

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38

Deventer, Rainer. Modeling and control of static and dynamic systems with Bayesian networks. Berlin: Logos, 2004.

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39

Luyben, William L. Process modelling, simulation and control for chemical engineers. Maidenhead: McGraw, 1990.

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40

S, Tzou H., and Bergman Lawrence A, eds. Dynamics and control of distributed systems. Cambridge, [Eng.]: Cambridge University Press, 1998.

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41

1936-, May Robert M., ed. Infectious diseases of humans: Dynamics and control. Oxford: Oxford University Press, 1991.

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42

W, Barnett Michael, Stenstrom Michael K, and Andrews John F, eds. Dynamics and control of wastewater systems. 2nd ed. Lancaster, PA: Technomic Publishing Co., Inc., 1998.

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43

Franco, Bagnoli, Ruffo Stefano 1954-, Institute for Scientific Interchange, European Commission, and EU Advanced Workshop on Dynamical Modeling in Biotechnology (1996 : Turin, Italy), eds. Lectures presented at the EU Advanced Workshop on Dynamical Modeling in Biotechnology: DGXII European Commission BIOTECH (1994-1998) Programme : Villa Gualino, Torino, Italy, 27 May-9 June 1996. Singapore: World Scientific, 2001.

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44

Chaotic Dynamics of Sea Clutter (Adaptive and Learning Systems for Signal Processing, Communications and Control Series). Wiley-Interscience, 1999.

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45

Ferrari, Matthew. Using disease dynamics and modeling to inform control strategies in low-income countries. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198789833.003.0008.

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The incidence infectious disease is inherently dynamic in time and space. Mathematical models that account for the dynamic processes that give rise to fluctuations in disease incidence are powerful tools in disease management and control. We describe the use of dynamic models for surveillance, evaluation and prediction of disease control efforts in low-income countries. Dynamic models can help to anticipate trends owing to intrinsic (e.g., herd immunity) or extrinsic (e.g., seasonality) forces that may confound efforts to isolate the impact of specific interventions. Infectious disease dynamics are frequently nonlinear, meaning that future outcomes are difficult to predict through simple extrapolation of present conditions. Thus, dynamic models can help to explore the potential consequences of proposed interventions. These projections can alert managers to the potential for unintended consequences of control and help to define effect sizes for the design of conventional studies of the impact of interventions.

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46

Beyreuther, Roland, and Harald Brünig. Dynamics of Fibre Formation and Processing. Springer, 2006.

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47

Borisov, Andrey Valerievich, and Anatoly Vlasovich Chigarev. Mathematical Models of Exosceleton: Dynamics, Strength, Control. Springer International Publishing AG, 2022.

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48

Denkena, Berend, and Ferdinand Hollmann. Process Machine Interactions: Predicition and Manipulation of Interactions between Manufacturing Processes and Machine Tool Structures. Springer, 2014.

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49

Mikles, Ján, and Miroslav Fikar. Process Modelling, Identification, and Control. Springer, 2007.

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50

Mikles, Ján, and Miroslav Fikar. Process Modelling, Identification, and Control. Springer London, Limited, 2007.

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