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Books on the topic 'Deterministic networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Kumar, Bose Deb, ed. Neural networks: Deterministic methods of analysis. London: International Thomson Computer Press, 1996.

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2

Moore, Kevin L. Iterative learning control for deterministic systems. London: Springer-Verlag, 1993.

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3

Wang, Cong. Deterministic learning theory for identification, control, and recognition. Boca Raton: CRC Press, 2009.

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4

Bitran, Gabriel R. Multiproduct queueing networks with deterministic routing: Decomposition approach and the notion of interference. Cambridge, Mass: Sloan School of Management, Massachusetts Institute of Technology, 1986.

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5

Wang, Jun. A Bayesian classifier based on a deterministic annealing neural network for aircraft fault classification. Wright-Patterson AFB, OH: Human Resources Directorate, Logistics Research Division, U.S. Air Force Armstrong Laboratory, 1997.

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6

Bouillard, Anne, Marc Boyer, and Euriell Le Corronc. Deterministic Network Calculus. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119440284.

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7

Ahsen, Mehmet Eren, Hitay Özbay, and Silviu-Iulian Niculescu. Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15606-4.

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8

Galland, Conrad. Learning in deterministic Boltzmann machine networks. 1992.

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9

Boyer, Marc, Anne Bouillard, and Euriell Le Corronc. Deterministic Network Calculus: From Theory to Practical Implementation. Wiley & Sons, Incorporated, John, 2018.

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10

Lente, Gábor. Deterministic Kinetics in Chemistry and Systems Biology: The Dynamics of Complex Reaction Networks. Springer, 2015.

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11

J, Hill David, and Cong Wang. Deterministic Learning Theory for Identification, Recognition, and Control. Taylor & Francis Group, 2017.

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12

Deterministic Learning Theory for Identification, Control, and Recognition (Automation and Control Engineering Series). CRC, 2008.

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13

Karttunen, Lauri. Finite-State Technology. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0018.

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The article introduces the basic concepts of finite-state language processing: regular languages and relations, finite-state automata, and regular expressions. Many basic steps in language processing, ranging from tokenization, to phonological and morphological analysis, disambiguation, spelling correction, and shallow parsing, can be performed efficiently by means of finite-state transducers. The article discusses examples of finite-state languages and relations. Finite-state networks can represent only a subset of all possible languages and relations; that is, only some languages are finite-state languages. Furthermore, this article introduces two types of complex regular expressions that have many linguistic applications, restriction and replacement. Finally, the article discusses the properties of finite-state automata. The three important properties of networks are: that they are epsilon free, deterministic, and minimal. If a network encodes a regular language and if it is epsilon free, deterministic, and minimal, the network is guaranteed to be the best encoding for that language.

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14

Tashayod, Alex *. Deterministic local area network protocols. 1988.

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15

Unger, Herwig, and Wolfgang A. Halang, eds. Autonomous Systems 2016. VDI Verlag, 2016. http://dx.doi.org/10.51202/9783186848109.

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To meet the expectations raised by the terms Industrie 4.0, Industrial Internet and Internet of Things, real innovations are necessary, which can be brought about by information processing systems working autonomously. Owing to their growing complexity and their embedding in complex environments, their design becomes increasingly critical. Thus, the topics addressed in this book span from verification and validation of safety-related control software and suitable hardware designed for verifiability to be deployed in embedded systems over approaches to suppress electromagnetic interferences to strategies for network routing based on centrality measures and continuous re-authentication in peer-to-peer networks. Methods of neural and evolutionary computing are employed to aid diagnosing retinopathy of prematurity, to invert matrices and to solve non-deterministic polynomial-time hard problems. In natural language processing, interface problems between humans and machines are solved with g...

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16

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Phenomenological Modelling. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0009.

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Detecting causal interactions among climatic, environmental, and human forces in complex biophysical systems is essential for understanding how these systems function and how public policies can be devised that protect the flow of essential services to biological diversity, agriculture, and other core economic activities. Convergent Cross Mapping (CCM) detects causal networks in real-world systems diagnosed with deterministic, low-dimension, and nonlinear dynamics. If CCM detects correspondence between phase spaces reconstructed from observed time series variables, then the variables are determined to causally interact in the same dynamic system. CCM can give false positives by misconstruing synchronized variables as causally interactive. Extended (delayed) CCM screens for false positives among synchronized variables.

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17

Niculescu, Silviu-Iulian, Hitay Özbay, and Mehmet Eren Ahsen. Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays. Birkhäuser, 2015.

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18

Newman, Stuart A. Toward a Nonidealist Evolutionary Synthesis. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199377176.003.0006.

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The received model of evolution sees all inherited features resulting from deterministic networks of interacting genes, implying that living systems are reducible to information in genetic programs. The model requires these programs and their associated phenotypes to have evolved by an isotropic search process occurring in gradual steps with no preferred morphological outcomes. The alternative is to recognize that clusters and aggregates of cells, the raw material of evolution, constitute middle-scale material systems. This implies the necessity of bringing the modern physics of mesoscale matter into the explanatory framework for the evolution of development. The relevant, often nonlinear, physical processes were mobilized at the inception of the phyla when their signature morphological outcomes first appeared and remain as efficient causes, albeit transformed, in present-day embryos. This physicogenetic perspective reengages with concepts of saltation, orthogenesis, and environment-induced plasticity long excluded from evolutionary theory.

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19

Boudec, Jean-Yves Le, and Patrick Thiran. Network Calculus: A Theory of Deterministic Queuing Systems for the Internet (Lecture Notes in Computer Science). Springer, 2001.

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