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Books on the topic 'Artificial Neural Network-based modeling'

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Author: Grafiati
Published: 11 March 2023

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1

Artificial neural network modeling of water and wastewater treatment processes. Hauppauge, N.Y: Nova Science Publishers, 2010.

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2

Khataee, A. R. Artificial neural network modeling of water and wastewater treatment processes. Hauppauge, N.Y: Nova Science Publishers, 2010.

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3

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

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4

Shanmuganathan, Subana, and Sandhya Samarasinghe, eds. Artificial Neural Network Modelling. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8.

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5

S, Mohan. Artificial neural network modelling. Roorkee: Indian National Committee on Hydrology, 2007.

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6

National Hydrology Symposium (4th 1993 Cardiff, Wales). Rainfall-runoff modeling as a problem in artificial intelligence: experience with a neural network. Fourth National Hydrology Symposium: (held at) University of Wales College of Cardiff 13-16th September 1993. (London?): British Hydrological Society, 1993.

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7

Thrun, Sebastian. Explanation-Based Neural Network Learning: A Lifelong Learning Approach. Boston, MA: Springer US, 1996.

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8

Thrun, Sebastian. Explanation-based neural network learning: A lifelong learning approach. Boston: Kluwer Academic Publishers, 1996.

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9

White, Roger. The artificial intelligence of urban dynamics: Neural network modelling of urban structure. [Toronto]: Centre for Urban and Community Studies, University of Toronto, 1989.

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10

Daniel, Sarit. Wavelet based artificial neural network and entropy detection techniques for a chaosmaker. Ottawa: National Library of Canada, 2002.

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11

Tucci, Mario, and Marco Garetti, eds. Proceedings of the third International Workshop of the IFIP WG5.7. Florence: Firenze University Press, 2002. http://dx.doi.org/10.36253/88-8453-042-3.

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Contents of the papers presented at the international workshop deal with the wide variety of new and computer-based techniques for production planning and control that has become available to the scientific and industrial world in the past few years: formal modeling techniques, artificial neural networks, autonomous agent theory, genetic algorithms, chaos theory, fuzzy logic, simulated annealing, tabu search, simulation and so on. The approach, while being scientifically rigorous, is focused on the applicability to industrial environment.
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12

T, Bialasiewicz Jan, and Langley Research Center, eds. Neural network modeling of nonlinear systems based on Volterra series extension of a linear model. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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13

Dolk, Daniel. Modeling for Decision Support in Network-Based Services: The Application of Quantitative Modeling to Service Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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14

M, Senthilkumar, and Bhabha Atomic Research Centre, eds. A self-learning approach based on artificial neural network (ANN) for the characterization, analysis and inverse synthesis of thin film optical coatings. Mumbai: Bhabha Atomic Research Centre, 2001.

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15

Di, Wei, Anurag Bhardwaj, and Jianing Wei. Deep Learning Essentials: Your hands-on guide to the fundamentals of deep learning and neural network modeling. Packt Publishing, 2018.

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16

Samarasinghe, Sandhya, and Subana Shanmuganathan. Artificial Neural Network Modelling. Springer, 2018.

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17

Samarasinghe, Sandhya, and Subana Shanmuganathan. Artificial Neural Network Modelling. Springer London, Limited, 2016.

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18

Samarasinghe, Sandhya, and Subana Shanmuganathan. Artificial Neural Network Modelling. Springer, 2016.

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19

Butyrskiy, Evgeniy, and Alexandr Matveev. Mathematical modeling of systems and processes. Strategy of the Future, 2022. http://dx.doi.org/10.37468/book_011222.

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The monograph considers the fundamentals of systems theory and mathematical modeling using the principles of the systems approach. In the monograph, much attention is paid to set-theoretic, dynamic, as well as aggregative and combined models. Based on the group-theoretical approach, a generalization of the theory of signals, their characteristics are considered, their classification and some theorems are carried out. A separate chapter is devoted to mathematical models of signal interaction with the propagation medium. The monograph also considers mathematical models of stochastic signal processing and control systems based on spline filtering, artificial intelligence models and neural networks. The monograph can be useful for a wide range of specialists in various fields of knowledge dealing with mathematical modeling in their research, and can also be used as a teaching aid for conducting both classroom and independent theoretical and practical classes with bachelors, masters, graduate students in the discipline "System Theory and System Analysis", "Mathematical Modeling" and "Optimal and Suboptimal Estimation of Random Processes and Systems".
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20

Artificial Neural Network-Based Optimized Design of Reinforced Concrete Structures. CRC Press LLC, 2022.

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21

Won‐Kee Hong. Artificial Neural Network-Based Optimized Design of Reinforced Concrete Structures. Taylor & Francis Group, 2023.

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22

Artificial Neural Network-Based Optimized Design of Reinforced Concrete Structures. CRC Press LLC, 2022.

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23

Won‐Kee Hong. Artificial Neural Network-Based Optimized Design of Reinforced Concrete Structures. Taylor & Francis Group, 2023.

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24

Won‐Kee Hong. Artificial Neural Network-Based Optimized Design of Reinforced Concrete Structures. Taylor & Francis Group, 2023.

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25

Explanation-Based Neural Network Learning: A Lifelong Learning Approach. Springer, 2012.

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26

Zohuri, Bahman, and Masoud Moghaddam. Neural Network Driven Artificial Intelligence: Decision Making Based on Fuzzy Logic. Nova Science Publishers, Incorporated, 2017.

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27

Artificial neural network-based methodologies for rational assessment of remaining life of existing pavements. El Paso, TX: Center for Highway Materials Research, University of Texas at El Paso, 1999.

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28

Granat, Janusz, and Daniel Dolk. Modeling for Decision Support in Network-Based Services: The Application of Quantitative Modeling to Service Science. Springer, 2012.

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29

Hands-On Neuroevolution with Python: Build High-Performing Artificial Neural Network Architectures Using Neuroevolution-based Algorithms. Packt Publishing, Limited, 2019.

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30

Churchland, Patricia S., and Terrence J. Sejnowski. The Computational Brain. The MIT Press, 2018. http://dx.doi.org/10.7551/mitpress/9780262533393.001.0001.

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Before this book was published in 1992, conceptual frameworks for brain function were based on the behavior of single neurons, applied globally. This book developed a different conceptual framework, based on large populations of neurons. This was done by showing that patterns of activities among the units in trained artificial neural network models had properties that resembled those recorded from populations of neurons recorded one at a time. It is one of the first books to bring together computational concepts and behavioral data within a neurobiological framework. Aimed at a broad audience of neuroscientists, computer scientists, cognitive scientists, and philosophers, the book is written for both expert and novice. This anniversary edition offers a new preface by the authors that puts the book in the context of current research. This approach influenced a generation of researchers in the field of neuroscience. Even today, when neuroscientists can routinely record from hundreds of neurons using optics rather than electricity, and the 2013 White House BRAIN initiative heralded a new era in innovative neurotechnologies, the main message of this book is still relevant.
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