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Books on the topic 'Neural controller'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 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

Dinu, Andrei. FPGA neural controller for three-phase sensorless induction motor drive systems. De Montfort University, 2000.

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5

United States. National Aeronautics and Space Administration., ed. Knowledge-based aircraft automation: Managers guide on the use of artificial intelligence for aircraft automation and verification and validation approach for a neural-based flight controller. National Aeronautics and Space Administration, 1997.

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6

United States. National Aeronautics and Space Administration., ed. Knowledge-based aircraft automation: Managers guide on the use of artificial intelligence for aircraft automation and verification and validation approach for a neural-based flight controller. National Aeronautics and Space Administration, 1997.

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7

United States. National Aeronautics and Space Administration., ed. Knowledge-based aircraft automation: Managers guide on the use of artificial intelligence for aircraft automation and verification and validation approach for a neural-based flight controller. National Aeronautics and Space Administration, 1997.

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8

United States. National Aeronautics and Space Administration., ed. Knowledge-based aircraft automation: Managers guide on the use of artificial intelligence for aircraft automation and verification and validation approach for a neural-based flight controller. National Aeronautics and Space Administration, 1997.

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9

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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10

Fowler, Kim. Computer controlled neurological stimulation system: Hardware and firmware descriptions. Johns Hopkins University/Applied Physics Laboratory, 1991.

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11

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

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12

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

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13

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

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14

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

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15

United States. National Aeronautics and Space Administration., ed. Learning fuzzy logic control system. National Aeronautics and Space Administration, 1994.

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16

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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17

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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18

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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19

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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20

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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21

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

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22

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

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23

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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24

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

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25

Miller, Earl K., and Timothy J. Buschman. Neural Mechanisms for the Executive Control of Attention. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.017.

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The prefrontal cortex is a source of internal control of attention as it captures three important components of an executive controller. First, it provides top-down selection of neural representations through descending projections, This top-down input may act by increasing the synchrony of local neural populations, enhancing their connectivity, and boosting the transmission of information. Second, intelligent top-down control of behaviour requires integrating diverse information. Neural representations in prefrontal cortex capture this breadth of information: representing anything from the sp

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26

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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27

National Aeronautics and Space Administration (NASA) Staff. Distributed Memory Approaches for Robotic Neural Controllers. Independently Published, 2018.

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28

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 res

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29

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

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30

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

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31

Pfaff, D. W. Estrogens and Brain Function: Neural Analysis of a Hormone-Controlled Mammalian Reproductive Behavior. Springer, 2011.

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32

Estrogens and Brain Function: Neural Analysis of a Hormone-Controlled Mammalian Reproductive Behavior. Springer, 2011.

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33

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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34

Pfaff, D. W. Estrogens and Brain Function: Neural Analysis of a Hormone-Controlled Mammalian Reproductive Behavior. Springer London, Limited, 2012.

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35

Salay, Nancy. How Words Help Us Think. Bloomsbury Publishing Plc, 2025. https://doi.org/10.5040/9781350266858.

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A capacity to act for reasons is a key indicator of intelligence. A leaf floats this way and that as the wind currents shift, a drone moves up or down with the movements of its controller, but a cognitive agent decides to walk to the store to get some food. This deliberative capacity to think through hypothetical situations, to choose between the grocery store or the restaurant, requires representational intentionality, the ability to think about real and possible situations in the world. According to the mainstream zeitgeist in the cognitive sciences, this capacity exhaustively reduces to low

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36

Bowman, Ryan, Hannah Schwennesen, Kafui Dzirasa, and Rainbo Hultman. In Vivo Circuit Analysis. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0007.

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Breakthroughs in understanding neural circuit activity hold much promise for developing next generation therapeutics for psychiatric disorders. Determination of how dynamic activity is coordinated across brain regions to effect specific behavioral function (or dysfunction) enables the development of therapeutics with increased specificity and fewer side effects. This chapter discusses methodologies for measuring neural circuit activity in humans and in animal models, and describes a bidirectional research pipeline whereby studies in humans are followed by tightly controlled studies in animal m

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37

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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38

Redes Neurais: Com Aplicações em Controle e em Sistemas Especialistas. Visual Books, 2000.

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39

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 crossm

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40

Quinn, Roger D., and Roy E. Ritzmann. Principles and mechanisms learned from insects and applied to robotics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0042.

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This chapter explores how insects have provided inspiration for robotic designs over recent decades as biologists have continued to learn more about these complex invertebrate systems. Initial legged robots typically mimicked insects only in terms of their basic six-legged (hexapod) designs and walking gaits. Since then robots have been developed that take advantage of insect leg and wing designs, compliant structures, movement behaviors, reflexes, and even local neural control systems identified in their central nervous systems. Future robots may be controlled with models of entire insect ner

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41

Holtmann, Martin, Björn Albrecht, and Daniel Brandeis. Neurofeedback. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198739258.003.0039.

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Neurofeedback of specific brain activity patterns allows perceiving and learning to gain control over these otherwise unaware neuronal processes. Neurofeedback may improve underlying neuronal deficits, and/or establish more general self-regulatory skills for compensating behavioural difficulties in other domains. Treating ADHD is the most common clinical neurofeedback application. Standard neurofeedback protocols based on electroencephalography train self-regulation of oscillatory activity in certain frequency bands (targeting theta/beta ratio) or slow cortical potential shifts. Both protocols

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42

Clites, Tyler R. A neurally controlled, single degree of freedom, active orthosis to restore ankle plantarflexion in a paralyzed rabbit. 2014.

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43

Seedat, Soraya, and Sharain Suliman. PTSD in Low- and Middle-Income Countries. Edited by Charles B. Nemeroff and Charles R. Marmar. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190259440.003.0009.

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The burden of post-traumatic stress disorder (PTSD) in low- and middle-income countries (LMIC) is high. In addition to trauma type and sociodemographic correlates, cultural and social drivers of the disorder are critical to consider in diagnosis and treatment provision. Sociocultural factors may underpin the cognitive and affective neural mechanisms of PTSD and its phenotypic presentation. Although steps have been taken to evaluate and disseminate empirically supported interventions for PTSD in LMIC that are feasible, affordable, and potentially sustainable despite the limited resources that e

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44

Burton, Derek, and Margaret Burton. Integration and control: hormones. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785552.003.0010.

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Fish, and other vertebrates, possess both a neural control system and a hormonal one dependent on the circulation. Fish hormones may be secreted by well-defined endocrine glands or by diffuse tissue and can have profound effects on the function of target tissues or organs which possess receptor molecules recognized by specific hormones. Some endocrine activities in fish are not relevant to mammals, and vice versa. Thus fish can have chromatophores controlled hormonally to change skin colour, the same hormones having different roles in mammals. Likewise, ‘prolactin’ in fish cannot regulate lact

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45

Cohen, Daniel A., and Asim Roy. Sleep and Neurological Disorders. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198778240.003.0010.

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Scientific investigation of the relationships between sleep and neurological disorders is at a relatively early stage. Damage to the nervous system or impaired neural development can cause a wide array of sleep disorders. In turn, sleep disruption may impair neuroplastic processes that are important for functional recovery after nervous system insults. Sleep disorders in patients with neurological disease can negatively affect quality of life for both the patients and the caregivers. Cardiovascular, metabolic, and immune process changes associated with sleep disorders may exacerbate the underl

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46

Rammah, Mayyasa, Francesca Rochais, and Robert G. Kelly. Incorporation of myocardial progenitors at the arterial pole of the heart. Edited by José Maria Pérez-Pomares, Robert G. Kelly, Maurice van den Hoff, et al. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198757269.003.0007.

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The arterial pole of the heart is a hotspot for life-threatening forms of congenital heart defects (CHDs). It is formed by progressive addition of myocardium from epithelial progenitor cells in the second heart field (SHF). SHF cells contribute successively to the right ventricle and proximal and distal outflow tract myocardial walls which, after neural crest influx and cardiac septation, give rise to myocardium at the base of the aorta and pulmonary trunk. SHF cells are characterized by continued proliferation and differentiation delay controlled by an array of transcriptional regulators and

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47

Russo, Francesca, Tim Van Mieghem, and Jan Deprest. Fetal medicine, fetal anaesthesia, and fetal surgery. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198713333.003.0007.

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Advances in prenatal imaging and the introduction of screening policies enable identification of high-risk pregnancies which can be followed up more meticulously. First-trimester evaluation is also used for assessment of risk for fetal anomalies. Further investigation may reveal a fetal anomaly. When the prognosis is poor, and treatment cannot wait until birth, fetal intervention may be warranted. This can be medical or surgical, some as simple as a needle-guided fetal blood transfusion. Over the last two decades, fetal surgery has become more popular, boosted by instrument development for min

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48

Stocchetti, Nino, and Marco Carbonara. Pharmacologic Neuroprotection. Edited by David L. Reich, Stephan Mayer, and Suzan Uysal. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190280253.003.0002.

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Acute cerebral injury sets into motion a cascade of deleterious biochemical events that cause further neuronal damage and amplify deleterious effects. This cascade develops over time and potentially may be attenuated or limited by pharmacologic manipulation. The neuroprotective properties of several molecules have been clearly demonstrated in experimental models of various pathologies. Based on these findings, many promising compounds have been tested in clinical trials. Large randomized controlled trials, however, have repeatedly failed to provide evidence of clinical efficacy. The authors pr

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49

Fomberstein, Kenneth, Marissa Rubin, Dipan Patel, John-Paul Sara, and Abhishek Gupta. Perioperative Opioid Analgesics of Use in Pain Management for Spine Surgery. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190626761.003.0004.

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This chapter compares the basic properties of several opioid analgesics and explores their applications in perioperative pain control in spine surgery. Parenteral opioids have long been the cornerstone of treatment for postoperative pain; they work by inhibiting voltage-gated calcium channels and increasing potassium influx, which results in reduced neuronal excitability, thereby inhibiting the ascending transmission of painful stimuli and activating the descending inhibitory pathways. This chapter reviews concepts including opioid conversion and rotation, opioid tolerance, and opioid cross-to

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50

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

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