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Books on the topic 'Training systems in artificial neural networks'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Guan, Biing T. Modeling training site vegetation coverage probability with a random optimization procedure: An artificial neural network approach. US Army Corps of Engineers, Construction Engineering Research Laboratories, 1998.

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2

United States. National Aeronautics and Space Administration., ed. Object-oriented control system design using on-line training of artificial neural networks: Final report, grant no. NAG3-1661, December 01, 1996 - April 30, 1997 ... Howard University/NASA Lewis cooperative research studies. Howard University, College of Engineering, Architecture and Computer Sciences, Electrical Engineering Dept., 1997.

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3

United States. National Aeronautics and Space Administration., ed. Object-oriented control system design using on-line training of artificial neural networks: Final report, grant no. NAG3-1661, December 01, 1996 - April 30, 1997 ... Howard University/NASA Lewis cooperative research studies. Howard University, College of Engineering, Architecture and Computer Sciences, Electrical Engineering Dept., 1997.

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4

United States. National Aeronautics and Space Administration., ed. Object-oriented control system design using on-line training of artificial neural networks: Final report, grant no. NAG3-1661, December 01, 1996 - April 30, 1997 ... Howard University/NASA Lewis cooperative research studies. Howard University, College of Engineering, Architecture and Computer Sciences, Electrical Engineering Dept., 1997.

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5

United States. National Aeronautics and Space Administration., ed. Object-oriented control system design using on-line training of artificial neural networks: Final report, grant no. NAG3-1661, December 01, 1996 - April 30, 1997 ... Howard University/NASA Lewis cooperative research studies. Howard University, College of Engineering, Architecture and Computer Sciences, Electrical Engineering Dept., 1997.

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6

Zurada, Jacek M. Introduction to artificial neural systems. West, 1992.

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7

Stefan, Wermter, and Sun Ron 1960-, eds. Hybrid neural systems. Springer, 2000.

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8

Anastassiou, George A. Intelligent Systems: Approximation by Artificial Neural Networks. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21431-8.

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9

Anastassiou, George A. Intelligent Systems: Approximation by Artificial Neural Networks. Springer Berlin Heidelberg, 2011.

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10

Elmasry, Mohamed I. VLSI Artificial Neural Networks Engineering. Springer US, 1994.

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11

Dorsey, Robert E. Bankruptcy prediction using artificial neural systems. Research Foundation of The Institute of Chartered Financial Analysts, 1995.

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12

Kattan, Ali. Artificial neural network training and software implementation techniques. Nova Science Publishers, 2011.

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13

Fakhraie, Sied Mehdi. VLSI - Compatible Implementations for Artificial Neural Networks. Springer US, 1997.

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14

Zgurovsky, Michael, Victor Sineglazov, and Elena Chumachenko. Artificial Intelligence Systems Based on Hybrid Neural Networks. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-48453-8.

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15

Kattan, Ali. Artificial neural network training and software implementation techniques. Nova Science Publishers, 2011.

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16

Kattan, Ali. Artificial neural network training and software implementation techniques. Nova Science Publishers, 2011.

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17

L, Silva F., Príncipe J. C, and Almeida L. B, eds. Spatiotemporal models in biological and artificial systems. Ohmsha, 1997.

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18

Group, Yankee, ed. Artificial intelligence in telecommunications: Expert systems and neural networks. The Group, 1989.

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19

Delgado-Frias, José G., and Will R. Moore. VLSI for neural networks and artificial intelligence. Springer Science+Business Media, 1994.

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20

Corsten, Hans. Artificial neural networks to support production planning and control systems. Wirtschaftswissenschaftliche Fakultät Ingolstadt, Katholische Universität Eichstätt, 1995.

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21

Eeckman, Frank H. Neural Systems: Analysis and Modeling. Springer US, 1993.

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22

Hudson, D. L. Neural networks and artificial intelligence for biomedical engineering. Institute of Electrical and Electronics Engineers, 2000.

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23

E, Cohen M., ed. Neural networks and artificial intelligence for biomedical engineering. Institute of Electrical and Electronics Engineers, 2000.

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24

Suykens, Johan A. K. Artificial Neural Networks for Modelling and Control of Non-Linear Systems. Springer US, 1996.

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25

Nino, Boccara, and NATO Advanced Study Institute on Cellular Automata and Cooperative Systems (1992 : Les Houches, Haute-Savoie, France), eds. Cellular automata and cooperative systems. Kluwer Academic, 1993.

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26

Eeckman, Frank H. Analysis and Modeling of Neural Systems. Springer US, 1992.

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27

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin Heidelberg, 2012.

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28

Suykens, Johan A. K., Joos P. L. Vandewalle, and Bart L. R. De Moor. Artificial Neural Networks for Modelling and Control of Non-Linear Systems. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2493-6.

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29

1948-, Vandewalle J., and Moor, Bart L. R. de, 1960-, eds. Artificial neural networks for modelling and control of non-linear systems. Kluwer Academic Publishers, 1996.

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30

L, Palumbo Daniel, Arras Michael K, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. Fault tolerance of artificial neural networks with applications in critical systems. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1992.

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31

Hyötyniemi, Heikki. Self-organizing artificial neural networks in dynamic systems modeling and control. Helsinki University of Technology, 1994.

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32

Workshop, on Virtual Intelligence/Dynamic Neural Networks (9th 1998 Stockholm Sweden). Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Academic/industrial/NASA/defense technical interchange and tutorials : international conferences on virtual intelligence/dynamic neural networks--neural networks, fuzzy systems, evolutionary systems, and virtual reality/pulse coupled neural networks, 1998. SPIE, 1999.

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33

Magoulas, George D. Investigations into living systems, artificial life, and real-world solutions. Information Science Reference, 2013.

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34

1962-, Kinser Jason M., Lindblad Thomas, Padgett Mary Lou, et al., eds. Proceedings, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Academic/Industrial/NASA/Defense: Technical interchange and tutorials : international conferences on virtual intelligence/dynamic neural networks: neural networks, fuzzy systems, evolutionary systems and virtual reality/pulse coupled neural networks, 1998. SPIE, 1999.

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35

1948-, Sethi Ishwar K., and Jain Anil K. 1948-, eds. Artificial neural networks and statistical pattern recognition: Old and new connections. North-Holland, 1991.

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36

Teshnehlab, Mohammad. Intelligent Control Based on Flexible Neural Networks. Springer Netherlands, 1999.

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37

Kosko, Bart. Neural networks and fuzzy systems: A dynamical systems approach to machine intelligence. Prentice-Hall, 1992.

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38

Artificial Neural Networks. Springer, 2010.

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39

(Editor), Cihan H. Dagli, C. L. Chen (Editor), and Metin Akay (Editor), eds. Intelligent Engineering Systems Through Artificial Neural Networks: Proceedings of the 1998 Artificial Neural Networks in Engineering Conference (Annie ... Systems Through Artificial Neural Networks). American Society of Mechanical Engineers, 1995.

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40

Wermter, Stefan, and Ron Sun. Hybrid Neural Systems. Springer, 2006.

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41

Dagli, Cihan H., K. Mark Bryden, Steven M. Corns, Mitsuo Gen, Kagan Tumer, and Gürsel Süer, eds. Intelligent Engineering Systems through Artificial Neural Networks. ASME Press, 2009. http://dx.doi.org/10.1115/1.802953.

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42

Intelligent Engineering Systems Through Artificial Neural Networks. Amer Society of Mechanical, 2001.

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43

Dagli, Cihan H. Intelligent Engineering Systems Through Artificial Neural Networks. Amer Society of Mechanical, 2000.

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44

Anastassiou, George A. A. Intelligent Systems: Approximation by Artificial Neural Networks. Springer, 2013.

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45

Intelligent Engineering Systems Through Artificial Neural Networks. Amer Society of Mechanical Engineers, 1992.

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46

Dagli, Cihan H. Intelligent Engineering Systems Through Artificial Neural Networks. Amer Society of Mechanical, 2004.

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47

Anastassiou, George A. Intelligent Systems: Approximation by Artificial Neural Networks. Springer, 2012.

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48

Dagli, Cihan H. Intelligent Engineering Systems Through Artificial Neural Networks (Intelligent Engineering Systems Through Artificial Neural Ne). American Society of Mechanical Engineers, 1993.

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49

Boden, Margaret A. 4. Artificial neural networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/actrade/9780199602919.003.0004.

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Abstract:

Artificial neural networks (ANNs) are made up of many interconnected units, each one capable of computing only one thing. ANNs have myriad applications, from playing the stock market and monitoring currency fluctuations to recognizing speech or faces. ANNs are parallel-processing virtual machines implemented on classical computers. They are intriguing partly because they are very different from the virtual machines of symbolic AI. Sequential instructions are replaced by massive parallelism, top-down control by bottom-up processing, and logic by probability. ‘Artificial neural networks’ considers the wider implications of ANNs and discusses parallel distributed processing (PDP), learning in neural networks, back-propagation, deep learning, and hybrid systems.

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50

Dagli, Cihan H., Laura I. Burke, and Yung C. Shin. Intelligent Engineering Systems Through Artificial Neural Networks: Proceedings of the Artificial Neural Networks in Engineering (Annie '92). American Society of Mechanical Engineers, 1992.

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