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Books on the topic 'Parameter'

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Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2024

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1

Coca, D. Direct parameter identification of distributed parameter systems. Sheffield: University of Sheffield, Dept. of Automatic Control and Syste,s Engineering, 1998.

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2

Kuhn, Jonathan Richard Dixon. Parameter forcing. Toronto: [s.n.], 1994.

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3

Roeper, Thomas, and Edwin Williams, eds. Parameter Setting. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3727-7.

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4

Thomas, Roeper, and Williams Edwin, eds. Parameter setting. Dordrecht: D. Reidel Pub. Co., 1987.

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5

Kappel, Franz, Karl Kunisch, and Wilhelm Schappacher, eds. Distributed Parameter Systems. Berlin/Heidelberg: Springer-Verlag, 1987. http://dx.doi.org/10.1007/bfb0041979.

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6

Kappel, Franz, Karl Kunisch, and Wilhelm Schappacher, eds. Distributed Parameter Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/bfb0005641.

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7

Alexandrova, Galina M., and Olga Arnaudova, eds. The Minimalist Parameter. Amsterdam: John Benjamins Publishing Company, 2001. http://dx.doi.org/10.1075/cilt.192.

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8

Lebbe, Luc C. Hydraulic Parameter Identification. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60117-0.

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9

Tibshirani, Robert. Non-resistant parameter. Toronto: University of Toronto, Dept. of Statistics, 1986.

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10

The polysynthesis parameter. New York: Oxford University Press, 1996.

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11

Bondre-Beil, Priyamvada. Parameter der Syntax. Tübingen: M. Niemeyer Verlag, 1994.

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12

Jiao, Jiujiu. Sensitivity features of aquifer parameters and their implications on parameter estimation. Birmingham: University of Birmingham, 1993.

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13

Newbold, Paul, and Theodore Bos. Stochastic Parameter Regression Models. 2455 Teller Road, Newbury Park California 91320 United States of America: SAGE Publications, Inc., 1985. http://dx.doi.org/10.4135/9781412985994.

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14

Smith, Carlota S. The Parameter of Aspect. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5606-6.

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15

Smith, Carlota S. The Parameter of Aspect. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7911-7.

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16

Volponi, Allan J. Gas Turbine Parameter Corrections. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41076-6.

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17

Bock, Hans Georg, Thomas Carraro, Willi Jäger, Stefan Körkel, Rolf Rannacher, and Johannes P. Schlöder, eds. Model Based Parameter Estimation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30367-8.

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18

Jaeggli, Osvaldo A., and Kenneth J. Safir, eds. The Null Subject Parameter. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2540-3.

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19

Shin, Dong Ryong. Parameter plane design method. Monterey, Calif: Naval Postgraduate School, 1989.

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20

Winiarska, Teresa. Differential equations with parameter. Cracow: T. Kościuszko technical University of Cracow, 1988.

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21

Theodore, Bos, ed. Stochastic parameter regression models. Beverly Hills: Sage Publications, 1985.

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22

Smith, Carlota S. The parameter of aspect. 2nd ed. Dordrecht: Kluwer, 1997.

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23

Theodore, Bos, ed. Stochastic parameter regression models. Beverly Hills: Sage Publications, 1985.

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24

Andres, Terry. SYVAC3 parameter distribution package. Pinawa, Man: Whiteshell Laboratories, [AECL Research], 1995.

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25

Cole, M. D. The pro-drop parameter. Salford: University of Salford, 1994.

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26

Rochman, Meuthia G. HAM sebagai parameter pembangunan. Jakarta: ELSAM, 1997.

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27

Smith, Carlota S. The parameter of aspect. Dordrecht: Kluwer Academic Publishers, 1991.

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28

Osvaldo, Jaeggli, and Safir Kenneth J, eds. The Null subject parameter. Dordrecht: Kluwer Academic Publishers, 1989.

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29

Cohen, A. Clifford. Parameter estimation in reliability and life span models. New York: M. Dekker, 1988.

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30

Auriol, Jean, Joachim Deutscher, Guilherme Mazanti, and Giorgio Valmorbida, eds. Advances in Distributed Parameter Systems. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94766-8.

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31

Butkovskiy, A. G., and L. M. Pustyl’nikov. Characteristics of Distributed-Parameter Systems. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2062-3.

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32

Gutman, Shaun, ed. Root Clustering in Parameter Space. Berlin/Heidelberg: Springer-Verlag, 1990. http://dx.doi.org/10.1007/bfb0042758.

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33

Lobo, Fernando G., Cláudio F. Lima, and Zbigniew Michalewicz, eds. Parameter Setting in Evolutionary Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-69432-8.

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34

Sun, Ne-Zheng, and Alexander Sun. Model Calibration and Parameter Estimation. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2323-6.

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35

Kirchknopf, Peter. Ermittlung modaler Parameter aus Übertragungsfrequenzgängen. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-75093-9.

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36

van Eeden, Constance. Restricted Parameter Space Estimation Problems. New York, NY: Springer New York, 2006. http://dx.doi.org/10.1007/978-0-387-48809-7.

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37

Temath, Bettina. Kulturelle Parameter in der Werbung. Wiesbaden: VS Verlag für Sozialwissenschaften, 2011. http://dx.doi.org/10.1007/978-3-531-92635-3.

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38

Peters, Martin. Classification of two-parameter bifurcations. [s.l.]: typescript, 1991.

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39

Gutman, S. Root clustering in parameter space. Berlin: Springer-Verlag, 1990.

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40

Parameter setting in language acquisition. London: Continuum, 2003.

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41

Iliff, Kenneth W. Bibliography for aircraft parameter estimation. Edwards, Calif: Dryden Flight Research Facility, 1986.

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42

Martinez-Velasco, Juan A. Power system transients: Parameter determination. Boca Raton, FL: CRC Press, 2010.

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43

Kirchknopf, Peter. Ermittlung modaler Parameter aus Übertragungsfrequenzgängen. Berlin: Springer, 1989.

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44

Chu, Chia-Shang J. Mosum tests for parameter constancy. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1992.

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45

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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46

Vaez-Zadeh, Sadegh. Parameter Estimation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198742968.003.0007.

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In this chapter, the estimation of permanent magnetic synchronous (PMS) motor parameters, including stator winding resistance, motor inductances, and magnitude of permanent magnet flux linage, is presented in two main categories, i.e., offline and online. Several offline schemes, including DC and AC standstill tests, no-load test, load test, and vector control schemes, are presented for estimation of all the motor parameters. Major online schemes used in the estimation of PMS motor parameters are also presented in this chapter. They include closed-loop observer-based estimation, model reference adaptive system (MRAS)-based estimation, recursive least-squares (RLS) estimation, and extended Kalman filter scheme. The online schemes take into account the motor parameter variations during motor operation. The motor model, estimation procedure, and the connection of estimation systems to the motor control system are discussed for each parameter estimation scheme.

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47

Parameter Setting. Springer, 2011.

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48

Roeper, Thomas, and Edwin Williams. Parameter Setting. Springer London, Limited, 2012.

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49

Cheng, Russell. Randomized-Parameter Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0013.

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This chapter does not involve non-standard behaviour but is included as a contribution to the broader book theme on model building. The basic idea is to obtain greater flexibility in fitting a standard two-parameter base distribution by multiplying one of its parameters by a one-parameter mixing random variable with mean unity. Absorbing the random effect by integration yields what will be called a randomized parameter (also called compound) distribution depending on all three parameters involved. This chapter collects together a large number of examples where there is a gamma mixing distribution. Their tail behaviour is compared. For the cases where the base distribution is the Pearson Type III or V, the randomized three-parameter model is the Pearson Type VI, providing a different view of the relationship between these distributions previously examined via embeddedness. Fits obtained using some of these models in a real-data example are given.

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50

One-parameter semigroups. Amsterdam: North-Holland, 1987.

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