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Books on the topic 'Neural network controller'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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1

Haynes, B. P. A neural network adaptive controller for non-linear systems. University of Portsmouth, Faculty of Technology, 1997.

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2

Denise, Taylor Lynore, and United States. National Aeronautics and Space Administration., eds. Artificial neural network implementation of a near-ideal error prediction controller. Dept. of Electrical Engineering, School of Engineering and Applied Science, University of Virginia, 1992.

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3

Jorgensen, Charles C. Development of a sensor coordinated kinematic model for neural network controller training. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.

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4

Jorgensen, Charles C. Distributed memory approaches for robotic neural controllers. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.

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5

Rylatt, R. Mark. Investigations into controllers for adaptive autonomous agents based on artificial neural networks. De Montfort University, 2001.

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6

Blake, Joseph. Neural network controllers: Software implementation and a hardware implementation based on a reconfigurable computing application. The Author], 1996.

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7

1944-, Nguyen Hung T., ed. A first course in fuzzy and neural control. Chapman & Hall/CRC Press, 2003.

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8

1944-, Nguyen Hung T., ed. A first course in fuzzy and neural control. Chapman & Hall/CRC Press, 2003.

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9

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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10

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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11

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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12

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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13

N, Lea Robert, Villarreal James, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic: Proceedings of a workshop sponsored by the National Aeronautics and Space Administration ... and cosponsored by Lyndon B. Johnson Space Center and the University of Houston, Clear Lake, Houston, Texas, April 10-13, 1990. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1991.

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14

Takao, Kumazawa, Kruger Lawrence, and Mizumura Kazue, eds. The polymodal receptor: A gateway to pathological pain. Elsevier, 1996.

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15

Adaptive Neural Network Controller for ATM Traffic. Storming Media, 1996.

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16

Artificial neural network implementation of a near-ideal error prediction controller. Dept. of Electrical Engineering, School of Engineering and Applied Science, University of Virginia, 1992.

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17

Lang, Michael. A real-time implementation of a neural-network controller for industrial robotics. 1998.

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18

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Translational controller results. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1992.

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19

Repole, Donato. Research of Parallel Computing Neuro-fuzzy Networks for Unmanned Vehicles. RTU Press, 2021. http://dx.doi.org/10.7250/9789934226922.

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The Doctoral Thesis illustrates the author’s research in the field of VHDL based ‘neuro-fuzzy controllers’. The Thesis examines a novel software tool for the high-level ‘neuro-fuzzy controller’ description capable of executing controller simulations, optimisation tasks, performing learning / training tasks, and exporting the controller in VHDL code. The author introduces a design strategy that is looking for developing solutions for complex controller architecture of mobile robotic vehicles (of any nature) or even for multiple industrial application. This work enables further investigative research into autonomous robotics, particularly into the physical implementation of an autonomous aerial unmanned vehicle from an inexpensive RC plane.

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20

Supervised Sequence Labelling With Recurrent Neural Networks. Springer, 2012.

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21

Two neural network algorithms for designing optimal terminal controllers with open final-time. NASA Ames Research Center, 1992.

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22

National Aeronautics and Space Administration (NASA) Staff. Two Neural Network Algorithms for Designing Optimal Terminal Controllers with Open Final Time. Independently Published, 2018.

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23

Prasad, Nadipuram R., Carol L. Walker, Elbert A. Walker, and Hung T. Nguyen. First Course in Fuzzy and Neural Control. Taylor & Francis Group, 2002.

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24

Spence, Charles. Orienting Attention. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.015.

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The last 30 years or so have seen a rapid rise in research on attentional orienting from a crossmodal perspective. The majority of this research has tended to focus on the consequences of the covert orienting of attention (either to a sensory modality or spatial location) for both perception and neural information processing. The results of numerous studies have now highlighted the robust crossmodal links that exist in the case of both overt and covert, and both exogenous and endogenous spatial orienting. Neuroimaging studies have started to highlight the neural circuits underlying such crossmodal effects. Researchers are increasingly using transcranial magnetic stimulation in order to lesion temporarily putative areas within these networks; the aim of such research often being to determine whether attentional orienting is controlled by supramodal versus modality-specific neural systems that are somehow linked (this is known as the ‘separable-but-linked’ hypothesis). The available research demonstrates that crossmodal attentional orienting (and multisensory integration—from which it is sometimes hard to distinguish) can affect the very earliest stages of information processing in the human brain.

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25

Russo, Marco. Fuzzy Learning and Applications. Taylor & Francis Group, 2019.

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26

Russo, Marco. Fuzzy Learning and Applications. Taylor & Francis Group, 2019.

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27

Russo, Marco. Fuzzy Learning and Applications. Taylor & Francis Group, 2019.

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28

Russo, Marco, and Lakhmi C. Jain. Fuzzy Learning and Applications (International Series on Computational Intelligence). CRC, 2000.

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29

Russo, Marco. Fuzzy Learning and Applications. Taylor & Francis Group, 2019.

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30

(Editor), T. Kumazawa, L. Kruger (Editor), and K. Mizumura (Editor), eds. The Polymodal Receptor - A Gateway to Pathological Pain (Progress in Brain Research). Elsevier Science, 1996.

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