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Books on the topic 'Parameter optimization'

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Published: 4 June 2021
Last updated: 29 January 2023

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1

Morelli, Eugene A. Practical input optimization for aircraft parameter estimation experiments. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1993.

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2

Romanovich, Statnikov Alexander, ed. The parameter space investigation method toolkit. Boston: Artech House, 2011.

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3

Optimal control of nonsmooth distributed parameter systems. Berlin: Springer-Verlag, 1990.

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4

Tiba, Dan. Optimal control of nonsmooth distributed parameter systems. Berlin: Springer-Verlag, 1990.

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5

Malanowski, Kazimierz, Zbigniew Nahorski, and Małgorzata Peszyńska, eds. Modelling and Optimization of Distributed Parameter Systems Applications to engineering. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-0-387-34922-0.

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6

Chen, Youyi. Extended quasi-likelihoods and optimal estimating functions. Toronto: University of Toronto, Dept. of Statistics, 1991.

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7

S, Korkhin Arnold, and SpringerLink (Online service), eds. Regression Analysis Under A Priori Parameter Restrictions. New York, NY: Springer Science+Business Media, LLC, 2012.

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8

Uciński, Dariusz. Optimal measurement methods for distributed parameter system identification. Boca Raton, Fla: CRC Press, 2005.

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9

Optimal measurement methods for distributed parameter system identification. Boca Raton, Fla: CRC Press, 2005.

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10

Schmidt, Phillip. A parameter optimization approach to controller partitioning for integrated flight/propulsion control application. [Washington, DC: National Aeronautics and Space Administration, 1992.

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11

Böttcher, K. J. Efficiency comparison and parameter sensitivity of deterministic and stochastic search methods. Neubiberg: University of the Federal Armed Forces Munich, Faculty of Aero-Space Engineering, Institute of Mathematics and Computer Sciences, 1996.

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12

Generalized optimal control of linear systems with distributed parameters. Dordrecht: Kluwer Academic Publishers, 2002.

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13

Bock, Hans Georg. Model Based Parameter Estimation: Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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14

Olivas, Frumen, Fevrier Valdez, Oscar Castillo, and Patricia Melin. Dynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-70851-5.

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15

Eremin, I. I. (Ivan Ivanovich), ed. International Conference on Distributed Systems: Optimization and Economic-Environmental Applications : DS0'2000 : proceedings, Ekaterinburg, Russia, 30 May-2 June, 2000. Ekaterinburg: Uralʹskoe otdelenie RAN, 2000.

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16

V, Kri͡azhimskiĭ A., Maksimov V. I, and Institut matematiki i mekhaniki (Akademii͡a nauk SSSR), eds. Zadachi dinamicheskoĭ reguli͡arizat͡sii dli͡a sistem s raspredelennymi parametrami. Sverdlovsk: UrO AN SSSR, 1991.

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17

IFIP WG 7.2 Conference (1989 Laxenburg, Austria). Modelling and inverse problems of control for distributed parameter systems: Proceedings of IFIP (W.G. 7.2), IIASA conference, Laxenburg, Austria, July 1989. Berlin: Springer-Verlag, 1991.

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18

Moayyedian, Mehdi. Intelligent Optimization of Mold Design and Process Parameters in Injection Molding. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03356-9.

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19

Gal, Tomas. Advances in Sensitivity Analysis and Parametic Programming. Boston, MA: Springer US, 1997.

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20

Mischenko, Aleksandr, and Anastasiya Ivanova. Optimization models for managing limited resources in logistics. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1082948.

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In the proposed monograph, optimization models for managing limited resources in logical systems are considered. Such systems are primarily used by industrial enterprises, transport companies and trade organizations, including those that carry out wholesale activities. As a rule, the efficiency of these objects largely depends on how rational use of limited resources such as: consumer camera business, labor, vehicles, etc. In this paper, various approaches to managing such resources are considered both for deterministic models and for the situation when a number of model parameters are not specified exactly, that is, for stochastic models. In this case, it is proposed to evaluate the stability of models to the occurrence of various types of risk events, both by the structure of the solution and by the functionality. It is addressed to senior students, postgraduates and masters studying in the specialty "Management" and "Logistics", as well as specialists in the field of logistics systems modeling.
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21

IUTAM Symposium on Field Analyses for Determination of Material Parameters-- Experimental and Numerical Aspects (2000 Kiruna, Sweden). IUTAM Symposium on Field Analyses for Determination of Material Parameters-- Experimental and Numerical Aspects. Boston: Kluwer Academic Publishers, 2003.

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22

Lerch, F. J. Optimum data weighting and error calibration for estimation of gravitational parameters. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1989.

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23

Bowers, Charles Emery. An assessment of competing facility location optimization parameters: A case study of the United States Navy, Morale Welfare and Recreation Facilities. Springfield, Va: Available from National Technical Information Service, 2000.

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24

Murav'ev, Dmitriy, Aleksandr Rahmangulov, Nikita Osincev, Sergey Kornilov, and Aleksandr Cyganov. The system "seaport - "dry" port". ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1816639.

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The monograph presents an approach to solving the problem of increasing the throughput and processing capacity of seaports in conditions of limiting their territorial dislocation and increasing the unevenness of external and internal cargo flows. The basis of the approach is the proposed system of the main parameters of the dry port and the methodology of simulation modeling of the functioning of the system "seaport - dry port". The material is illustrated with examples of the implementation of the developed approach, including model scenarios of multi-agent optimization of the parameters of the system under study. The proposed approach and the developed methodology can be used to justify management decisions on the balanced development of transport and logistics infrastructure of the regions hosting sea and dry ports. It is intended for specialists of transport and logistics companies, engineering and technical workers engaged in solving problems in the field of logistics, supply chain management and transport infrastructure design. In addition, it is recommended to students in the following programs: postgraduate studies 23.06.01 "Land transport engineering and technology" (focus "Transport and transport-technological systems of the country, its regions and cities, organization of production in transport") and 27.06.01 "Management in technical systems" (focus "Management of transportation processes"); master's degree 23.04.01 "Technology of transport processes" (profile "Organization of transportation and management in a single transport system"); bachelor's degree 38.03.02 "Management" (profile "Logistics") and 23.03.01 "Technology of transport processes".
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25

P, Stähle, and Sundin K. G, eds. IUTAM Symposium on Field Analyses for Determination of Material Parameters-- Experimental and Numerical Aspects: Proceedings of the IUTAM Symposium held in Abisko National Park, Kiruna, Sweden, July 31-August 4, 2000. Dordrecht: Kluwer Academic Publishers, 2003.

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26

Prussing, John E. Parameter Optimization. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198811084.003.0002.

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Parameter optimization is treated as an introduction. Unconstrained and constrained cases are analysed. Necessary and sufficient conditions are derived and illustrated. Parameter optimization utilizes the theory of ordinary maxima and minima. The problem is to determine the value of the m-vector u of independent parameters (decision variables) to minimize the cost function.
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27

(Editor), K. Malanowski, Z. Nahorski (Editor), and M. Peszynska (Editor), eds. Modelling and Optimization of Distributed Parameter Systems. Springer, 1996.

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28

Nonlinear Parameter Optimization Using R Tools. John Wiley & Sons Inc, 2014.

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29

Design Optimization: Taguchi's Robust Parameter Design. New York: McGraw-Hill, 2010.

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30

Nash, John C. Nonlinear Parameter Optimization Using R Tools. Wiley & Sons, Incorporated, John, 2014.

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31

Nash, John C. Nonlinear Parameter Optimization Using R Tools. Wiley & Sons, Incorporated, John, 2014.

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32

Nash, John C. Nonlinear Parameter Optimization Using R Tools. Wiley & Sons, Limited, John, 2014.

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33

Nash, John C. Nonlinear Parameter Optimization Using R Tools. Wiley & Sons, Incorporated, John, 2014.

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34

Kanatani, Kenichi. 3D Rotations : Parameter Computation and Lie-Algebra Based Optimization: Parameter Computation and Lie-Algebra Based Optimization. Taylor & Francis Group, 2020.

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35

Cea, Jean, and E. J. Haug. Optimization of Distributed Parameter Structures ― Volume I. Springer, 2011.

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36

Experiments: Planning, Analysis, and Parameter Design Optimization. Wiley-Interscience, 2000.

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37

Cea, Jean, and E. J. Haug. Optimization of Distributed Parameter Structures - Volume II. Springer, 2012.

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38

Kazimierz, Malanowski, and Mizukami Kōichi 1936-, eds. Analysis and algorithms of optimization problems. Berlin: Springer-Verlag, 1986.

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39

Kazimierz, Malanowski, Nahorski Zbigniew 1945-, Peszyńska Małgorzata, IFIP-WG 7. 2, International Federation for Information Processing., and International Conference on Modelling and Optimization of Distributed Parameter Systems with Applications to Engineering (1995 : Warsaw, Poland), eds. Modelling and optimization of distributed parameter systems: Applications to engineering : selected proceedings of the IFIP WG7.2 on Modelling and Optimization of Distributed Parameter Systems with Applications to Engineering, June 1995. London: Chapman & Hall, 1996.

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40

Kanatani, Kenichi. 3D Rotations: Parameter Computation and Lie-Algebra Based Optimization. Taylor & Francis Group, 2020.

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41

S, Holmes Michael, Ray Asok 1946-, and Lewis Research Center, eds. Design of life extending controls using nonlinear parameter optimization. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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42

3D Rotations: Parameter Computation and Lie-Algebra Based Optimization. Taylor & Francis Group, 2020.

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43

S, Holmes Michael, Ray Asok 1946-, and Lewis Research Center, eds. Design of life extending controls using nonlinear parameter optimization. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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44

Kanatani, Kenichi. 3D Rotations: Parameter Computation and Lie-Algebra Based Optimization. Taylor & Francis Group, 2020.

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45

Kanatani, Kenichi. 3D Rotations: Parameter Computation and Lie-Algebra Based Optimization. Taylor & Francis Group, 2020.

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46

Tiba, Dan. Optimal Control of Nonsmooth Distributed Parameter Systems. Springer London, Limited, 2006.

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47

Knopov, Pavel S., and Arnold S. Korkhin. Regression Analysis Under A Priori Parameter Restrictions. Springer, 2011.

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48

Knopov, Pavel S., and Arnold S. Korkhin. Regression Analysis Under A Priori Parameter Restrictions. Springer, 2013.

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49

Lyashko, S. I. Generalized Optimal Control of Linear Systems with Distributed Parameters (Applied Optimization). Springer, 2002.

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50

A parameter optimization approach to controller partitioning for integrated flight/propulsion control application. [Washington, DC: National Aeronautics and Space Administration, 1992.

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