Relevant bibliographies by topics / Artificial feedback

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Artificial feedback'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Artificial feedback"

1

Verbruggen, H. B., and K. J. Åström. "Artificial intelligence and feedback control." Annual Review in Automatic Programming 15 (January 1989): 1–11. http://dx.doi.org/10.1016/0066-4138(89)90002-5.

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Verbruggen, H. B., and K. J. Åström. "Artificial Intelligence and Feedback Control." IFAC Proceedings Volumes 22, no. 13 (1989): 1–11. http://dx.doi.org/10.1016/b978-0-08-040185-0.50006-3.

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Herzog, Sebastian, Christian Tetzlaff, and Florentin Wörgötter. "Evolving artificial neural networks with feedback." Neural Networks 123 (March 2020): 153–62. http://dx.doi.org/10.1016/j.neunet.2019.12.004.

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Joshi, Sandeep, and Satpal Singh Kushwaha. "Query Expansion using Artificial Relevance Feedback." International Journal of Computer Applications 44, no. 7 (2012): 41–45. http://dx.doi.org/10.5120/6279-8448.

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Pistohl, Tobias, Deepak Joshi, Gowrishankar Ganesh, Andrew Jackson, and Kianoush Nazarpour. "Artificial Proprioceptive Feedback for Myoelectric Control." IEEE Transactions on Neural Systems and Rehabilitation Engineering 23, no. 3 (2015): 498–507. http://dx.doi.org/10.1109/tnsre.2014.2355856.

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Li, Q. Q., Z. C. He, and Eric Li. "The feedback artificial tree (FAT) algorithm." Soft Computing 24, no. 17 (2020): 13413–40. http://dx.doi.org/10.1007/s00500-020-04758-2.

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Şahin, Savaş. "Learning Feedback Linearization Using Artificial Neural Networks." Neural Processing Letters 44, no. 3 (2015): 625–37. http://dx.doi.org/10.1007/s11063-015-9484-8.

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Rahman, Md Ataur, Sumeet Walia, Sumaiya Naznee, et al. "Artificial Somatosensors: Feedback Receptors for Electronic Skins." Advanced Intelligent Systems 2, no. 11 (2020): 2000094. http://dx.doi.org/10.1002/aisy.202000094.

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Rahman, Md Ataur, Sumeet Walia, Sumaiya Naznee, et al. "Artificial Somatosensors: Feedback Receptors for Electronic Skins." Advanced Intelligent Systems 2, no. 11 (2020): 2070106. http://dx.doi.org/10.1002/aisy.202070106.

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Badakva, A. M., N. V. Miller, and L. N. Zobova. "Artificial feedback for invasive brain–computer interfaces." Human Physiology 42, no. 1 (2016): 111–18. http://dx.doi.org/10.1134/s0362119716010023.

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Dissertations / Theses on the topic "Artificial feedback"

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Mohamad, Hanif Noor Hazrin Hany. "Sensory feedback for artificial hands." Thesis, University of Southampton, 2015. https://eprints.soton.ac.uk/391380/.

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Executing daily chores with missing limbs is undoubtedly very challenging. For a person who has lost his lower arm, it is highly desirable to replace this loss with a device that not only identical in appearance, but closely mimics its capabilities. While there are many prosthetic products of multiple functionalities in the current market, the capability of the device to replicate the tactile sensory system, the sensation largely felt at the fingertips and palm, is often neglected. The motivation of this work is to provide a sense of embodiment to prosthetic users by supplementing their device
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Boozarjomehry, Ramin B. "Application of artificial intelligence in feedback linearization." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/nq24528.pdf.

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Moore, Charles H. "Grasping Embodiment: Haptic Feedback for Artificial Limbs." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617107153868166.

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Puttlitz, Markus E. "Feedback heuristics for hard combinatorial optimization problems." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/24567.

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Fyfe, Colin. "Negative feedback as an organising principle for artificial neural networks." Thesis, University of Strathclyde, 1995. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21390.

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We investigate the properties of an unsupervised neural network which uses simple Hebbian learning and negative feedback of activation in order to self-organise. The negative feedback circumvents the well-known difficulty of positive feedback in Hebbian learning systems which causes the networks' weights to increase without bound. We show, both analytically and experimentally, that not only do the weights of networks with this architecture converge, they do so to values which give the networks important information processing properties: linear versions of the model are shown to perform a Prin
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Joyce, Bryan Steven. "Development of Active Artificial Hair Cell Sensors." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/52911.

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The cochlea is known to exhibit a nonlinear, mechanical amplification which allows the ear to detect faint sounds, improves frequency discrimination, and broadens the range of sound pressure levels that can be detected. In this work, active artificial hair cells (AHC) are proposed and developed which mimic the nonlinear cochlear amplifier. Active AHCs can be used to transduce sound pressures, fluid flow, accelerations, or another form of dynamic input. These nonlinear sensors consist of piezoelectric cantilever beams which utilize various feedback control laws inspired by the living cochlea
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Zhong, Junpei [Verfasser], and Stefan [Akademischer Betreuer] Wermter. "Artificial Neural Models for Feedback Pathways for Sensorimotor Integration / Junpei Zhong. Betreuer: Stefan Wermter." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2015. http://d-nb.info/1073248208/34.

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Ingram, Stephen D. "Visual Feedback Stabilisation of a Cart Inverted Pendulum A." Thesis, University of Bradford, 2016. http://hdl.handle.net/10454/17375.

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Vision-based object stabilisation is an exciting and challenging area of research, and is one that promises great technical advancements in the field of computer vision. As humans, we are capable of a tremendous array of skilful interactions, particularly when balancing unstable objects that have complex, non-linear dynamics. These complex dynamics impose a difficult control problem, since the object must be stabilised through collaboration between applied forces and vision-based feedback. To coordinate our actions and facilitate delivery of precise amounts of muscle torque, we primarily use o
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Prakash, Sunjeev. "Experimental analysis of artificial signal type and performance feedback delivery schedule in attenuating vigilance decrement." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ56718.pdf.

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Stallwood, James. "An artificial intelligence framework for feedback and assessment mechanisms in educational Simulations and Serious Games." Thesis, University of Southampton, 2015. https://eprints.soton.ac.uk/394643/.

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Simulations and Serious Games are powerful e-learning tools that can be designed to provide learning opportunities that stimulate their participants. To achieve this goal, the design of Simulations and Serious Games will often include some balance of three factors: motivation, engagement, and flow. Whilst many frameworks and approaches for Simulation and Serious Game design do provide the means for addressing a combination of these factors to some degree, few address how those factors might be affected by the presence of an out-of-game tutor. It is the position of some researchers that the pre
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Books on the topic "Artificial feedback"

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Willshire, Kelli F. Telerobotic hand controller study of force reflection with position control mode. National Aeronautics and Space Administration, Langley Research Center, 1992.

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Prats, Mario. Robot Physical Interaction through the combination of Vision, Tactile and Force Feedback: Applications to Assistive Robotics. Springer Berlin Heidelberg, 2013.

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Aubin, Jean Pierre. Viability theory. Birkhäuser, 2009.

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Aubin, Jean Pierre. Viability theory. Birkhäuser, 2009.

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Viability theory. Birkhauser, 1991.

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IEEE Conference on Decision and Control (34th 1995 New Orleans, La.). Proceedings of the 34th IEEE Conference on Decision and Control: December 13-15, 1995, New Orleans Hilton Riverside, New Orleans, Louisiana, USA. Institute of Electrical and Electronics Engineers, 1995.

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Leigh, J. R. Control Theory. 2nd ed. IET, 2004.

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Warwick, Kevin, Freddy Rafael Garces, Victor Manuel Becerra, and Chandrasekhar Kambhampati. Strategies for Feedback Linearisation. Springer, 2003.

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Prats, Mario, Ángel P. del Pobil, and Pedro J. Sanz. Robot Physical Interaction through the combination of Vision, Tactile and Force Feedback: Applications to Assistive Robotics. Springer, 2014.

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1973-, Garces Freddy, ed. Strategies for feedback linearisation: A dynamic neural network approach. Springer, 2003.

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Book chapters on the topic "Artificial feedback"

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Ginty, Lorraine Mc, and Barry Smyth. "Evaluating Preference-Based Feedback in Recommender Systems." In Artificial Intelligence and Cognitive Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45750-x_28.

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Swain, Basanta Kumar, Sanghamitra Mohanty, and Dillip Ranjan Nayak. "Extractive summarization of recorded Odia spoken feedback." In Artificial Intelligence and Speech Technology. CRC Press, 2021. http://dx.doi.org/10.1201/9781003150664-19.

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Noh, Tae-Gil, Jae-Kul Lee, Seong-Bae Park, Seyoung Park, Sang-Jo Lee, and Kweon-Yang Kim. "Tag Quality Feedback: A Framework for Quantitative and Qualitative Feedback on Tags of Social Web." In PRICAI 2010: Trends in Artificial Intelligence. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15246-7_62.

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Wieringa, Wilko, Harm op den Akker, Valerie M. Jones, Rieks op den Akker, and Hermie J. Hermens. "Ontology-Based Generation of Dynamic Feedback on Physical Activity." In Artificial Intelligence in Medicine. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22218-4_7.

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Kumar, Amruth N. "Generation of Demand Feedback in Intelligent Tutors for Programming." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24840-8_35.

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Salojärvi, Jarkko, Kai Puolamäki, and Samuel Kaski. "Implicit Relevance Feedback from Eye Movements." In Artificial Neural Networks: Biological Inspirations – ICANN 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550822_80.

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Michler, Frank, Thomas Wachtler, and Reinhard Eckhorn. "Adaptive Feedback Inhibition Improves Pattern Discrimination Learning." In Artificial Neural Networks in Pattern Recognition. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11829898_3.

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Cruse, Holk, Christian Bartling, and Thomas Kindermann. "High-pass filtered positive feedback for decentralized control of cooperation." In Advances in Artificial Life. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-59496-5_334.

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Feigl, Josef, and Martin Bogdan. "Improved Personalized Rankings Using Implicit Feedback." In Artificial Neural Networks and Machine Learning – ICANN 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01418-6_37.

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Dadarlat, Maria C., Joseph E. O’Doherty, and Philip N. Sabes. "A Learning-Based Approach to Artificial Sensory Feedback." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09979-8_4.

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Conference papers on the topic "Artificial feedback"

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Xie, Ruobing, Cheng Ling, Yalong Wang, Rui Wang, Feng Xia, and Leyu Lin. "Deep Feedback Network for Recommendation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/349.

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Both explicit and implicit feedbacks can reflect user opinions on items, which are essential for learning user preferences in recommendation. However, most current recommendation algorithms merely focus on implicit positive feedbacks (e.g., click), ignoring other informative user behaviors. In this paper, we aim to jointly consider explicit/implicit and positive/negative feedbacks to learn user unbiased preferences for recommendation. Specifically, we propose a novel Deep feedback network (DFN) modeling click, unclick and dislike behaviors. DFN has an internal feedback interaction component th
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Hanif, N. H. H. Mohamad, P. H. Chappell, A. Cranny, and N. M. White. "Vibratory feedback for artificial hands." In 2013 International Conference on Electronics, Computer and Computation (ICECCO). IEEE, 2013. http://dx.doi.org/10.1109/icecco.2013.6718275.

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Wang, Lei, and Licheng Jiao. "New feedback artificial neural network." In International Symposium on Multispectral Image Processing, edited by Ji Zhou, Anil K. Jain, Tianxu Zhang, Yaoting Zhu, Mingyue Ding, and Jianguo Liu. SPIE, 1998. http://dx.doi.org/10.1117/12.323575.

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Shanshan Li and Gang Tao. "Adaptive feedback control based artificial pancreas." In 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008. http://dx.doi.org/10.1109/wcica.2008.4593262.

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Løvlid, R. A., and P. Öztürk. "Dancing YMCA with Delayed Sensory Feedback." In Artificial Intelligence and Applications. ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.674-076.

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Gurnani, Ekta, and Sheena Gupta. "Decision Feedback Equalizer using Artificial Neural Networks." In Proceedings of the International Conference on Advances in Computer Science and Electronics Engineering. Research Publishing Services, 2012. http://dx.doi.org/10.3850/978-981-07-1403-1_623.

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Chen, Daniel, Thor Besier, Iain Anderson, and Thomas McKay. "Optimal haptic feedback control of artificial muscles." In SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, edited by Yoseph Bar-Cohen. SPIE, 2014. http://dx.doi.org/10.1117/12.2046491.

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Steiner, T., L. Schramm, Y. Jin, and B. Sendhoff. "Emergence of feedback in artificial gene regulatory networks." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424561.

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Sabes, Philip N., Maria C. Dadarlat, and Joseph E. O'Doherty. "A learning-based approach to artificial sensory feedback." In 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7171917.

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Joyce, Bryan S., and Pablo A. Tarazaga. "Active Artificial Hair Cells Using Nonlinear Feedback Control." In ASME 2014 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/smasis2014-7419.

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There is interest in developing devices that mimic the sound transduction of the cochlear hair cells. Current artificial hair cell (AHC) designs have focused on passive transduction of sound into electrical signals. However, measurements inside living cochleae have revealed that a nonlinear amplification is at work in mammalian hearing. This amplification lowers the threshold for sound detection allowing mammals to hear faint sounds. The nonlinearity results in an amplitude compression whereby a large range of sound pressure levels produces a smaller range of displacements. This compressive no
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Reports on the topic "Artificial feedback"

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Ruderman, Marian, and Cathleen Clerkin. The Quantified Leader: Wearables & Self-Tracking Technology for Development. Center for Creative Leadership, 2019. http://dx.doi.org/10.35613/ccl.2019.2052.

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The quantified leader uses self-tracking technology, paired with wearables and artificial intelligence, for continuous, personalized data and feedback. Download this white paper to learn about the use of these technologies for development.
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