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Books on the topic 'Iterative model'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Diane, Ullius, and American Society for Training and Development., eds. Designing powerful training: The sequential-iterative model. Jossey-Bass/Pfeiffer, 1998.

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2

Waddell, Dale R. Estimating the weight of Douglas-fir tree boles and logs with an iterative computer model. U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 1987.

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3

Waddell, Dale R. Estimating the weight of Douglas-fir tree boles and logs with an iterative computer model. U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 1987.

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4

Wachspress, Eugene. The ADI Model Problem. Springer New York, 2013.

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5

Duflo, Marie. Random Iterative Models. Springer Berlin Heidelberg, 1997.

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6

Duflo, Marie. Random Iterative Models. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-12880-0.

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7

Duflo, Marie. Random iterative models. Springer, 1997.

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8

Pol, Marjon van der. Using a conditioned iterative generalised least squares estimator (CIGLS) to model a multilevel cost function and a discount function applied to repeated observations. Health Economics Research Unit, University of Aberdeen, 1997.

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9

Dubanov, Aleksandr. Computer simulation in pursuit problems. Publishing Center RIOR, 2022. http://dx.doi.org/10.29039/02102-6.

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Currently, computer simulation in virtual reality systems has a special status. In order for a computer model to meet the requirements of the tasks it models, it is necessary that the mathematical apparatus correctly describe the simulated phenomena. In this monograph, the simulation of pursuit problems is carried out. An adaptive modeling of the behavior of both pursuers and targets is carried out. An iterative calculation of the trajectories of the participants in the pursuit problem is carried out. The main attention is paid to the methods of pursuit and parallel rendezvous. The

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10

Chan, Raymond H. Iterative methods for overflow queueing models. Courant Institute of Mathematical Sciences, New York University, 1985.

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11

Sawatzki, Rainer. Iterative Verfahren zur Lösung der stationären Halbleitergleichungen. Verlag an der Lottbek, 1989.

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12

P, Smith Robert. Testing an engineering design iteration model in an experimental setting. Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1992.

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13

Sankoh, O. A. Influential observations in the linear regression model and Trenkler's iteration estimator. Cuvillier, 1996.

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14

Dudkin, L. M. Iterative aggregation techniques in Soviet national economic planning: A Delphic report. Delphic Associates, 1987.

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15

DeSarbo, Wayne. CRISP: Customer Response-based Iterative Segmentation Procedures for response modeling in direct marketing. Marketing Science Institute, 1994.

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16

DeSarbo, Wayne. CRISP: Customer Response-based Iterative Segmentation Procedures for response modeling in direct marketing. Marketing Science Institute, 1994.

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17

Chernykh, V. A. Metody funkt︠s︡ionalʹnykh iterat︠s︡iĭ v matematicheskoĭ fizike i modelirovanii dobychi gaza. Izdatelʹskiĭ Dom "Neftʹ-gaz", 2009.

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18

1952-, Thomas James L., and Institute for Computer Applications in Science and Engineering., eds. Solving upwind-biased discretizations: Defect-correction iterations. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1999.

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19

1952-, Thomas James L., and Institute for Computer Applications in Science and Engineering., eds. Solving upwind-biased discretizations: Defect-correction iterations. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1999.

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20

Diskin, Boris. Solving upwind-biased discretizations: Defect-correction iterations. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1999.

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21

Voorhies, Coerte V. On the joint inversion of geophysical data for models of the coupled core-mantle system. National Aeronautics and Space Administration, Goddard Space Flight Center, 1991.

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22

Carter, Richard G. Numerical optimization in Hilbert space using inexact function and gradient evaluations. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1989.

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23

C, Chung Wilson, Smith Mark J. T, and United States. National Aeronautics and Space Administration., eds. Subband image coding with jointly optimized quantizers. National Aeronautics and Space Administration, 1995.

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24

Zhu, Lian-di. A streamline-iteration method for calculating turbulent flow around the stern of a body of revolution and its wake. China Scientific Research Center, 1986.

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25

Voronin, Evgeniy, Aleksandr Chibunichev, and Yuriy Blohinov. Reliability of solving inverse problems of analytical photogrammetry. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/2010462.

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The monograph is devoted to computational aspects of photogrammetric reconstruction of narrow-angle bundles of projecting beams that existed during the survey. Methods of improving the conditionality of systems of linear equations, ensuring the convergence of iterative refinement of their roots, increasing the stability of calculations in finite precision machine arithmetic are considered. The main efforts are focused on solving the problem of establishing reliable measurement weights within the framework of the least squares method. The criteria for the reliability of the weights are determin

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26

S, Salzar Robert, and Langley Research Center, eds. Optimization of residual stresses in MMC's through process parameter control and the use of heterogeneous compensating/complaint interfacial layers: OPTCOMP2 user's guide. National Aeronautics and Space Administration, 1996.

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27

S, Salzar Robert, and Langley Research Center, eds. Optimization of residual stresses in MMC's through process parameter control and the use of heterogeneous compensating/complaint interfacial layers: OPTCOMP2 user's guide. National Aeronautics and Space Administration, 1996.

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28

Milano, Michael, and Diane Ullius. Designing Powerful Training: The Sequential-Iterative Model. Center for Creative Leadership, 2008.

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29

The Adi Model Problem. Springer, 2012.

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30

Iterative Business Model Canvas Development - from Vision to Product Backlog. Notion Press, 2021.

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31

From Model-Based to Data-Driven Discrete-Time Iterative Learning Control. [publisher not identified], 2019.

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32

Chi, Ronghu, Huimin Zhang, and Ruikun Zhang. Discrete-Time Adaptive Iterative Learning Control: From Model-Based to Data-driven. Springer Singapore Pte. Limited, 2022.

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33

Discrete-Time Adaptive Iterative Learning Control: From Model-Based to Data-Driven. Springer, 2023.

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34

Synthesis and Analysis of Design Methods in Linear Repetitive, Iterative Learning and Model Predictive Control. [publisher not identified], 2018.

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35

Wachspress, Eugene. The ADI Model Problem. Springer, 2015.

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36

Duflo, Marie. Random Iterative Models. Springer-Verlag Telos, 1997.

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37

Wymeersch, Henk. Iterative Receiver Design. Cambridge University Press, 2007.

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38

Wymeersch, Henk. Iterative Receiver Design. Cambridge University Press, 2010.

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39

Wymeersch, Henk. Iterative Receiver Design. Cambridge University Press, 2008.

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40

Iterative Receiver Design. Cambridge University Press, 2007.

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41

Boudreau, Joseph F., and Eric S. Swanson. Nonlinear dynamics and chaos. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0013.

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Simple maps and dynamical systems are used to explore chaos in nature. The discussion starts with a review of the properties of nonlinear ordinary differential equations, including the useful concepts of phase portraits, fixed points, and limit cycles. These notions are developed further in an examination of iterative maps that reveal chaotic behavior. Next, the damped driven oscillator is used to illustrate the Lyapunov exponent that can be used to quantify chaos. The famous KAM theorem on the conditions under which chaotic behavior occurs in physical systems is also presented. The principle

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42

Wymeersch, Henk. Iterative Receiver Design. Cambridge University Press, 2007.

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43

Wymeersch, Henk. Iterative Receiver Design. Cambridge University Press, 2007.

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44

Magerko, Brian. A Computationally Motivated Approach to Cognition Studies in Improvisation. Edited by Benjamin Piekut and George E. Lewis. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199892921.013.22.

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This chapter presents the guiding design rationale for the Georgia Institute of Technology’s Digital Improv Project, which studies human cognition as a means of informing the creation of interactive narrative experiences. This work serves as an example of studying human co-creativity with the end goal of developing computer/human systems that have similar control, knowledge, and status in a creative task. The chapter describes the novel iterative design and development model used in the project and its relevance to practices in the broader interactive narrative community.

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45

Iterative adaptation of the bidimensional wall of the French T2 wind tunnel around a C5 axisymmetrical model: Infinite variation of the Mach number at zero incidence and a test at increased incidence. National Aeronautics and Space Administration, 1986.

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46

Duflo, Marie. Random Iterative Models (Stochastic Modelling and Applied Probability). Springer, 2000.

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47

ITERATIVE ALGORITHMS FOR MULTILAYER OPTIMIZING CONTROL. IMPERIAL COLLEGE PRESS, 2005.

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48

Oulasvirta, Antti, Per Ola Kristensson, Xiaojun Bi, and Andrew Howes, eds. Computational Interaction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.001.0001.

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This book presents computational interaction as an approach to explaining and enhancing the interaction between humans and information technology. Computational interaction applies abstraction, automation, and analysis to inform our understanding of the structure of interaction and also to inform the design of the software that drives new and exciting human-computer interfaces. The methods of computational interaction allow, for example, designers to identify user interfaces that are optimal against some objective criteria. They also allow software engineers to build interactive systems that a

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49

Lattman, Eaton E., Thomas D. Grant, and Edward H. Snell. Pushing the Envelope. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199670871.003.0014.

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Direct electron density determination from SAXS data opens up new opportunities. The ability to model density at high resolution and the implicit direct estimation of solvent terms such as the hydration shell may enable high-resolution wide angle scattering data to be used to calculate density when combined with additional structural information. Other diffraction methods that do not measure three-dimensional intensities, such as fiber diffraction, may also be able to take advantage of iterative structure factor retrieval. While the ability to reconstruct electron density ab initio is a major

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50

Eppinger, Steven Daniel, Smith Robert P, and Sloan School of Management. Testing an Engineering Design Iteration Model in an Experimental Setting. Creative Media Partners, LLC, 2018.

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