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Books on the topic 'Computer-based mathematical model'

Author: Grafiati
Published: 19 February 2023

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1

Berber, Ridvan. Nonlinear Model Based Process Control. Dordrecht: Springer Netherlands, 1998.

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2

Rodionov, Mihail, Ekaterina Guseva, Gennadiy Shabanov, Evgeniy Budkin, and Lidiya Kapkaeva. Ways and means of ensuring continuity of education between a school and a military university (based on the material of mathematics and computer science). ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1868917.

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The monograph builds a model of profiling the education of schoolchildren based on the implementation of an integrated elective course of military engineering orientation. The main means of ensuring the effective functioning of this model is a system of educational military-applied tasks, the solution of which is carried out with the help of a certain mathematical apparatus using computer support. It is shown that an important factor in the implementation of the continuity of relations between the school and the military university is the effective adaptation of junior cadets to the conditions of university education. Accordingly, the adaptive capabilities of the creative educational environment of the military university are revealed. For students (cadets), postgraduates (adjuncts) and teachers of military and pedagogical universities, as well as for a wide range of readers interested in military training.
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3

Hürlimann, Tony. Mathematical Modeling and Optimization: An Essay for the Design of Computer-Based Modeling Tools. Boston, MA: Springer US, 1999.

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4

Neural networks and intellect: Using model-based concepts. Oxford: Oxford University Press, 2001.

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5

Chen, Jie. Robust Model-Based Fault Diagnosis for Dynamic Systems. Boston, MA: Springer US, 1999.

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6

Agent-based computer simulation of dichotomous economic growth. Boston: Kluwer Academic, 2000.

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7

Xiao, Bai, Jian Cheng, and Edwin R. Hancock. Graph-based methods in computer vision: Developments and applications. Hershey: Information Science Reference, 2012.

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8

Koning, Kees de. Model-based reasoning about learner behaviour. Amsterdam: IOS Press, 1997.

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9

Panin, Giorgio. Model-based visual tracking: The OpenTL framework. Hoboken, N.J: Wiley, 2011.

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10

Chen, J. Robust model-based fault diagnosis for dynamic systems. Boston: Kluwer Academic Publishers, 1999.

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11

T, Harper D. A., ed. Numerical palaeobiology: Computer-based modelling and analysis of fossils and their distributions. Chichester: Wiley, 1999.

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12

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

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13

Physics-based deformable models: Applications to computer vision, graphics, and medical imaging. Boston: Kluwer Academic, 1997.

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14

Metaxas, Dimitris N. Physics-Based Deformable Models: Applications to Computer Vision, Graphics and Medical Imaging. Boston, MA: Springer US, 1997.

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15

Mathematical modeling and optimization: An essay for the design of computer-based modeling tools. Dordrecht: Kluwer Academic, 1999.

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16

Diergardt, Martin. Modeling scenarios for analyzing the risks of complex computer based information systems. Berlin: dissertation.de, 2007.

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17

J, Broughton, Krakow William, Pantelides Sokrates T, and Materials Research Society, eds. Computer-based microscopic description of the structure and properties of materials: Symposium held December 2-4, 1985, Boston, Massachusetts, U.S.A. Pittsburgh, Pa: Materials Research Society, 1986.

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18

MBR China 2006 (2006 Guangzhou, China). Model-based reasoning in science, technology, and medicine. Berlin: Springer, 2007.

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19

Martishin, Sergey, Vladimir Simonov, and Marina Hrapchenko. Databases: Design and development of information systems using MySQL DBMS and Go language. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1830834.

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The tutorial is intended for studying and creating databases and information systems and applications using the MySQL database management system and the Go programming language. The book consistently introduces students to the installation and development of the MySQL Workbench workspace for working with MySQL DBMS; the basics of MySQL server administration; designing ER models in MySQL Workbench. The basics of the Go language are given. The creation of a project in the Go language is being considered. For the convenience of working with the textbook, program codes are placed in the cloud storage of the electronic library system of the publishing house "Znanium". Meets the requirements of the federal state educational standards of higher education of the latest generation and professional standards. For students, bachelors, undergraduates, postgraduates and specialists studying in the specialties and areas of training: "Computer Science and Computer Engineering", "Information systems and technologies", "Software Engineering", "Applied Mathematics and Computer Science", "Management in technical systems", "Business Informatics", "Pedagogical education", as well as for all persons engaged in the design of modern databases, information systems, the creation of mobile and Internet applications based on the MySQL database management system and the Go programming language.
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20

Modelling in public health research: How mathematical techniques keep us healthy. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan, 2015.

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21

Efremov, German. Modeling of chemical and technological processes. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1090526.

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In an accessible form, the textbook presents the theoretical foundations of physical and mathematical modeling; considers the modeling of mass, heat and momentum transfer processes, the relationship and analogy between them; studies the theory of similarity, its application in modeling, models of the structure of flows in apparatuses. Experimental-statistical and experimental-analytical modeling methods are also described, which include "black box" methods, planning passive, active full and fractional factor experiments, and adjusting models based on the results of the experiment. At the same time, modeling of chemical reactors, methods of optimization of chemical-technological processes, their selection, comparison and application examples are considered. Examples of modeling and optimization of processes in chemical, petrochemical and biotechnology on a computer in Excel and MathCAD environments are given. The appendices provide the basics of working in the MathCAD environment and elements of matrix algebra. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is intended for bachelors who are trained for the chemical, petrochemical, food, textile and light industries. It can be useful for specialists and undergraduates, as well as for scientists, engineers and postgraduates dealing with the problem under consideration.
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22

Crinon, Regis. Adaptive model-based motion estimation. 1993.

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23

Crinon, Regis. Adaptive model-based motion estimation. 1993.

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24

Anderson, James A. Brain Theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/acprof:oso/9780199357789.003.0015.

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“What is a number that a man may know it?” (Warren McCulloch). A wave model can determine “numerosity” (number of identical items) for small numbers of items. Identity and numerosity can be explained through similar mechanisms. Can there be a biology of number? Imaging studies find a topographic map of number magnitude in the human brain. Higher mathematics is based in part on refined perception. Classic mathematical philosophy—Platonism and formalism—may be usefully extended with perceptual components both learned and unlearned. Perceptual involvement suggests why mathematics is surprisingly good at dealing with the physical world. We find perceptual involvement even in simple integer multiplication. We can use “active” perceptual-based nets to program elementary abstract mathematical operations. A “brain-like” program is described for the “greater-than” program done by a digital computer in Chapter 4
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25

Perlovsky, Leonid I. Neural Networks and Intellect: Using Model-Based Concepts. Oxford University Press, USA, 2000.

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26

Multi-parametric model-based control. Weinheim: Wiley-VCH, 2006.

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27

McCain, Roger A. Agent-Based Computer Simulation of Dichotomous Economic Growth. Springer, 2012.

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28

McCain, Roger A. Agent-Based Computer Simulation of Dichotomous Economic Growth. Springer London, Limited, 2012.

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29

C, Georgiadis Michael, Pistikopoulos Efstratios N, and Dua Vivek, eds. Multi-parametric model-based control: Theory and applications. Weinheim: Wiley-VCH, 2007.

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30

Georgiadis, Michael C., Vivek Dua, and Efstratios N. Pistikopoulos. Multi-Parametric Model-Based Control: Theory and Applications. Wiley & Sons, Limited, John, 2011.

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31

Georgiadis, Michael C., Vivek Dua, and Efstratios N. Pistikopoulos. Multi-Parametric Model-Based Control: Theory and Applications. Wiley & Sons, Incorporated, John, 2021.

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32

Jäger, Willi, Hans Georg Bock, and Thomas Carraro. Model Based Parameter Estimation: Theory and Applications. Springer, 2013.

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33

Rannacher, Rolf, Willi Jäger, Hans Georg Bock, Thomas Carraro, Johannes P. Schlöder, and Stefan Körkel. Model Based Parameter Estimation: Theory and Applications. Springer, 2015.

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34

Philipp, Barbara Lee. Eigenstructure-based model reduction of linear control systems with applications. 1993.

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35

Gureckis, Todd M., and Bradley C. Love. Computational Reinforcement Learning. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.5.

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Reinforcement learning (RL) refers to the scientific study of how animals and machines adapt their behavior in order to maximize reward. The history of RL research can be traced to early work in psychology on instrumental learning behavior. However, the modern field of RL is a highly interdisciplinary area that lies that the intersection of ideas in computer science, machine learning, psychology, and neuroscience. This chapter summarizes the key mathematical ideas underlying this field including the exploration/exploitation dilemma, temporal-difference (TD) learning, Q-learning, and model-based versus model-free learning. In addition, a broad survey of open questions in psychology and neuroscience are reviewed.
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36

Process Systems Engineering: Volume 2: Multi-Parametric Model-Based Control (Process Systems Engineering). Wiley-VCH, 2007.

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37

Luh, John Zenyoung. Traffic-responsive strategies for personal-computer-based traffic signal control systems. 1989.

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38

Chung, Yeonbae. A physically based predictive lateral DMOS transistor model and circuit simulator for intelligent power IC design. 1995.

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39

Metaxas, Dimitris N. Physics-Based Deformable Models: Applications to Computer Vision, Graphics and Medical Imaging (The International Series in Engineering and Computer Science). Springer, 1996.

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40

Knowledge Structures for Communications in Human-Computer Systems: General Automata-Based (Practitioners). Wiley-IEEE Computer Society Pr, 2006.

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41

Albert, Kuo, ed. Development of an expert system based on a tidal prism water quality model for small coastal basins in Virginia. Gloucester Point, VA: School of Marine Science/Virginia Institute of Marine Science, The College of William and Mary, 1999.

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42

IEEE Computer Society. Proceedings of the Workshop on Physics-Based Modeling in Computer Vision: June 18-19, 1995 Cambridge, Massachusetts. IEEE Computer Society Press, 1995.

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43

Hedman, Shawn. A First Course in Logic. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780198529804.001.0001.

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The ability to reason and think in a logical manner forms the basis of learning for most mathematics, computer science, philosophy and logic students. Based on the author's teaching notes at the University of Maryland and aimed at a broad audience, this text covers the fundamental topics in classical logic in an extremely clear, thorough and accurate style that is accessible to all the above. Covering propositional logic, first-order logic, and second-order logic, as well as proof theory, computability theory, and model theory, the text also contains numerous carefully graded exercises and is ideal for a first or refresher course.
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44

Model-Based Reasoning about Learner Behaviour (Frontiers in Artificial Intelligence and Applications) (Frontiers in Artificial Intelligence and Applications, 44). Ios Pr Inc, 1997.

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45

Case-Based Approximate Reasoning (Theory and Decision Library B). Springer, 2007.

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46

Humphreys, Paul. Philosophical Papers. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780199334872.001.0001.

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Paul Humphreys pioneered philosophical investigations into the methodological revolution begun by computer simulations. He has also made important contributions to the contemporary literature on emergence by developing the fusion account of diachronic emergence and its generalization, transformational emergence. He is the discoverer of what has come to be called “Humphreys” Paradox in probability theory and has also made influential contributions to the literature on probabilistic causality and scientific explanation. This collection contains fourteen of his previously published papers on topics ranging from numerical experiments to the status of scientific metaphysics. There is also and a previously unpublished paper on social dynamics. The volume is divided into four parts on, respectively, computational science, emergence, probability, and general philosophy of science. The first part contains the seminal 1990 paper on computer simulations, with three other papers arguing that these new methods cannot be accounted for by traditional methodological approaches. The second part contains the original presentation of fusion emergence and three companion papers arguing for diachronic approaches to the topic, rather than the then dominant synchronic accounts. The third part starts with the paper that introduced the probabilistic paradox followed by a later evaluation of attempts to solve it. A third paper argues, contra Quine, that probability theory is a purely mathematical theory. The final part includes papers on causation, explanation, metaphysics, and an agent-based model that shows how endogenous uncertainty undermines utility maximization. Each of the four parts is followed by a comprehensive postscript with retrospective assessments.
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47

1944-, Earnshaw R. A., Parslow R. D. 1926-, Woodwark John, and Unicom Seminars, eds. Geometric modelling and computer graphics: Techniques and applications : based on papers delivered at seminars on 25-27 June 1985 organised by Unicom Seminars Ltd and on 26 February 1986 organised by BCS Graphics and Displays Group. (Aldershot): Technical Press in association with Unicom Seminars, 1987.

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48

Lenhard, Johannes. Calculated Surprises. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190873288.001.0001.

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In this book, Lenhard concentrates on the ways in which computers and simulation are transforming the established conception of mathematical modeling. His core thesis is that simulation modeling constitutes a new mode of mathematical modeling that is rearranging and inverting key features of the established conception. Although most of these new key features—such as experimentation, exploration, and epistemic opacity—have their precursors, the new ways in which they are being combined is generating a distinctive style of scientific reasoning. Lenhard also documents how simulation is affecting fundamental concepts of solution, understanding, and validation. He feeds these transformations back into the philosophy of science, thereby opening up new perspectives on longstanding oppositions. By combining historical investigations with practical aspects, the book is accessible for a broad audience of readers. Numerous case studies covering a wide range of simulation techniques are balanced with broad reflections on science and technology. Initially, what computers are good at is calculating—with a speed and accuracy far beyond human capabilities. Lenhard goes further and investigates the emerging characteristics of computer-based modeling, showing how this initially simple observation is creating a number of surprising challenges for the methodology and epistemology of science. These calculated surprises will attract both philosophers and scientific practitioners who are interested in reflecting on recent developments in science and technology.
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49

Mansnerus, Erika. Modelling in Public Health Research. Palgrave Macmillan, 2014.

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50

Kalinichenko, Evgeny. Theory and methods for calculating the inertial-braking characteristics of a ship. «Scientific Route» OÜ, 2020. http://dx.doi.org/10.21303/978-617-7319-30-5.

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One of the most serious problems of modern navigation is the accident rate that occurs due to inept or belated maneuvering of ships. As a result of accidents in the world, more than 200 ships die every year and every fourth receives significant damage. Full-scale tests show that the stopping distance of large-tonnage ships turn out to be much less permissible, and shipbuilders are able to significantly reduce the astern power of such ships, making them cheaper at the expense of safety. The low accuracy of inertial-braking characteristics is mainly due to unqualified field tests. Analysis of graphs and tables based on the results of such tests show that the spread in the values of inertial-braking characteristics for ships of the same type reaches 30%, and in some cases even more. In many tables and graphs, inertial-braking characteristics are expressed in relative values and are not suitable for direct use when maneuvering a ship. Finally, even when graphical and/or tabular maneuvering information is available on the navigating bridge, it is difficult to use it when maneuvering a ship at night. The research carried out by the author results in: - creation of an alternative computational method for determining the inertial-braking characteristics of the ship, suitable for use on any on-board computer; - development of an improved methodology for calculating the path and time of acceleration and braking of the ship in various ahead motion modes; - development of a methodology for taking into account the influence of a passing and opponent current on the length of the stopping distance of the ship; - development of methods for solving applied problems, ensuring a decrease in the accident rate of ships during maneuvering. The obtained methods include the development of theoretical foundations, mathematical models and comparison of the calculated inertial-braking characteristics of ships with the data of a full-scale experiment. For the first time, to derive the calculated formulas for the time and stopping distance, theorems are used on the change in the momentum and kinetic energy during accelerated and decelerated motion of the ship. In the course of the study, the problems of calculating and formalizing the inertial-braking characteristics of the ship are being comprehensively solved. For the first time, the hypothesis that the nature of the change in the thrust force of the propeller during reverse can be approximated by linear equations has been substantiated and confirmed. The general results are used to calculate the inertial-braking characteristics of specific ships.
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