Literatura académica sobre el tema "Supervised Motor Learning"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Supervised Motor Learning".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Supervised Motor Learning"
Xian, Xiaoyu, Haichuan Tang, Yin Tian, Qi Liu y Yuming Fan. "Performance Analysis of Different Machine Learning Algorithms for Identifying and Classifying the Failures of Traction Motors". Journal of Physics: Conference Series 2095, n.º 1 (1 de noviembre de 2021): 012058. http://dx.doi.org/10.1088/1742-6596/2095/1/012058.
Texto completoEt. al., Rameshwar D. Chintamani,. "Analysis of Motor Imagery EEG Classification Based on Feature Extraction and Machine Learning Algorithm". INFORMATION TECHNOLOGY IN INDUSTRY 9, n.º 2 (26 de marzo de 2021): 541–53. http://dx.doi.org/10.17762/itii.v9i2.381.
Texto completoSingh, Puneet, Sumitash Jana, Ashitava Ghosal y Aditya Murthy. "Exploration of joint redundancy but not task space variability facilitates supervised motor learning". Proceedings of the National Academy of Sciences 113, n.º 50 (29 de noviembre de 2016): 14414–19. http://dx.doi.org/10.1073/pnas.1613383113.
Texto completoShe, Zhou, Gan, Ma y Luo. "Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning". Electronics 8, n.º 11 (1 de noviembre de 2019): 1273. http://dx.doi.org/10.3390/electronics8111273.
Texto completoRaymond, Jennifer L. y Javier F. Medina. "Computational Principles of Supervised Learning in the Cerebellum". Annual Review of Neuroscience 41, n.º 1 (8 de julio de 2018): 233–53. http://dx.doi.org/10.1146/annurev-neuro-080317-061948.
Texto completoJigyasu, R., V. Shrivastava y S. Singh. "Prognostics and health management of induction motor by supervised learning classifiers". IOP Conference Series: Materials Science and Engineering 1168, n.º 1 (1 de julio de 2021): 012006. http://dx.doi.org/10.1088/1757-899x/1168/1/012006.
Texto completoZhang, Weiwei, Deji Chen y Yang Kong. "Self-Supervised Joint Learning Fault Diagnosis Method Based on Three-Channel Vibration Images". Sensors 21, n.º 14 (13 de julio de 2021): 4774. http://dx.doi.org/10.3390/s21144774.
Texto completoTang, Xian-Lun, Wei-Chang Ma, De-Song Kong y Wei Li. "Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition". Neural Computation 31, n.º 5 (mayo de 2019): 919–42. http://dx.doi.org/10.1162/neco_a_01183.
Texto completoPyle, Ryan y Robert Rosenbaum. "A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity". Neural Computation 31, n.º 7 (julio de 2019): 1430–61. http://dx.doi.org/10.1162/neco_a_01198.
Texto completoCingireddy, Anirudh Reddy, Robin Ghosh, Venkata Kiran Melapu, Sravanthi Joginipelli y Tor A. Kwembe. "Classification of Parkinson's Disease Using Motor and Non-Motor Biomarkers Through Machine Learning Techniques". International Journal of Quantitative Structure-Property Relationships 7, n.º 2 (abril de 2022): 1–21. http://dx.doi.org/10.4018/ijqspr.290011.
Texto completoTesis sobre el tema "Supervised Motor Learning"
Coen, Michael Harlan. "Multimodal dynamics : self-supervised learning in perceptual and motor systems". Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/34022.
Texto completoThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (leaves 178-192).
This thesis presents a self-supervised framework for perceptual and motor learning based upon correlations in different sensory modalities. The brain and cognitive sciences have gathered an enormous body of neurological and phenomenological evidence in the past half century demonstrating the extraordinary degree of interaction between sensory modalities during the course of ordinary perception. We develop a framework for creating artificial perceptual systems that draws on these findings, where the primary architectural motif is the cross-modal transmission of perceptual information to enhance each sensory channel individually. We present self-supervised algorithms for learning perceptual grounding, intersensory influence, and sensorymotor coordination, which derive training signals from internal cross-modal correlations rather than from external supervision. Our goal is to create systems that develop by interacting with the world around them, inspired by development in animals. We demonstrate this framework with: (1) a system that learns the number and structure of vowels in American English by simultaneously watching and listening to someone speak. The system then cross-modally clusters the correlated auditory and visual data.
(cont.) It has no advance linguistic knowledge and receives no information outside of its sensory channels. This work is the first unsupervised acquisition of phonetic structure of which we are aware, outside of that done by human infants. (2) a system that learns to sing like a zebra finch, following the developmental stages of a juvenile zebra finch. It first learns the song of an adult male and then listens to its own initially nascent attempts at mimicry through an articulatory synthesizer. In acquiring the birdsong to which it was initially exposed, this system demonstrates self-supervised sensorimotor learning. It also demonstrates afferent and efferent equivalence - the system learns motor maps with the same computational framework used for learning sensory maps.
by Michael Harlan Coen.
Ph.D.
De, La Bourdonnaye François. "Learning sensori-motor mappings using little knowledge : application to manipulation robotics". Thesis, Université Clermont Auvergne (2017-2020), 2018. http://www.theses.fr/2018CLFAC037/document.
Texto completoThe thesis is focused on learning a complex manipulation robotics task using little knowledge. More precisely, the concerned task consists in reaching an object with a serial arm and the objective is to learn it without camera calibration parameters, forward kinematics, handcrafted features, or expert demonstrations. Deep reinforcement learning algorithms suit well to this objective. Indeed, reinforcement learning allows to learn sensori-motor mappings while dispensing with dynamics. Besides, deep learning allows to dispense with handcrafted features for the state spacerepresentation. However, it is difficult to specify the objectives of the learned task without requiring human supervision. Some solutions imply expert demonstrations or shaping rewards to guiderobots towards its objective. The latter is generally computed using forward kinematics and handcrafted visual modules. Another class of solutions consists in decomposing the complex task. Learning from easy missions can be used, but this requires the knowledge of a goal state. Decomposing the whole complex into simpler sub tasks can also be utilized (hierarchical learning) but does notnecessarily imply a lack of human supervision. Alternate approaches which use several agents in parallel to increase the probability of success can be used but are costly. In our approach,we decompose the whole reaching task into three simpler sub tasks while taking inspiration from the human behavior. Indeed, humans first look at an object before reaching it. The first learned task is an object fixation task which is aimed at localizing the object in the 3D space. This is learned using deep reinforcement learning and a weakly supervised reward function. The second task consists in learning jointly end-effector binocular fixations and a hand-eye coordination function. This is also learned using a similar set-up and is aimed at localizing the end-effector in the 3D space. The third task uses the two prior learned skills to learn to reach an object and uses the same requirements as the two prior tasks: it hardly requires supervision. In addition, without using additional priors, an object reachability predictor is learned in parallel. The main contribution of this thesis is the learning of a complex robotic task with weak supervision
Ahnesjö, Henrik. "Fault detection of planetary gearboxes in BLDC-motors using vibration and acoustic noise analysis". Thesis, Uppsala universitet, Institutionen för elektroteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425966.
Texto completoSingh, Puneet. "The Role of Basal Ganglia and Redundancy in Supervised Motor Learning". Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4176.
Texto completoLibros sobre el tema "Supervised Motor Learning"
Herreros, Ivan. Learning and control. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0026.
Texto completoCapítulos de libros sobre el tema "Supervised Motor Learning"
Wazlawick, Raul Sidnei y Antonio Carlos da Rocha Costa. "Non-Supervised Sensory-Motor Agents Learning". En Artificial Neural Nets and Genetic Algorithms, 49–52. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_15.
Texto completoSmrkovsky, Eric y Hubert Cecotti. "Graph-Based Semi-supervised Learning Using Riemannian Geometry Distance for Motor Imagery Classification". En Lecture Notes in Computer Science, 317–26. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33783-3_30.
Texto completoJana, Gopal Chandra, Shivam Shukla, Divyansh Srivastava y Anupam Agrawal. "Performance Estimation and Analysis Over the Supervised Learning Approaches for Motor Imagery EEG Signals Classification". En Intelligent Computing and Applications, 125–41. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5566-4_12.
Texto completoPremkumar, K. y B. V. Manikandan. "Online Fuzzy Supervised Learning of Radial Basis Function Neural Network Based Speed Controller for Brushless DC Motor". En Lecture Notes in Electrical Engineering, 1397–405. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2119-7_136.
Texto completoToscano, David S. y Enrique V. Carrera. "Failure Detection in Induction Motors Using Non-supervised Machine Learning Algorithms". En Systems and Information Sciences, 48–59. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59194-6_5.
Texto completoKawato, Mitsuo. "Feedback-Error-Learning Neural Network for Supervised Motor Learning". En Advanced Neural Computers, 365–72. Elsevier, 1990. http://dx.doi.org/10.1016/b978-0-444-88400-8.50047-9.
Texto completoTrappenberg, Thomas P. "Motor Control and Reinforcement Learning". En Fundamentals of Computational Neuroscience, 323–62. 3a ed. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192869364.003.0011.
Texto completoBenarroch, Eduardo E. "Cerebellar Circuits". En Neuroscience for Clinicians, editado por Eduardo E. Benarroch, 610–30. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780190948894.003.0033.
Texto completoArbib, Michael A. "From empathy to mirror neurons and back to aesthetics". En When Brains Meet Buildings, 287–358. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780190060954.003.0005.
Texto completoActas de conferencias sobre el tema "Supervised Motor Learning"
Zaman, Shafi Md Kawsar, Xiaodong Liang y Lihong Zhang. "Induction Motor Fault Diagnosis Using Graph-Based Semi-Supervised Learning". En 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2020. http://dx.doi.org/10.1109/ccece47787.2020.9255706.
Texto completoBurfoot, D. y Y. Kuniyoshi. "Semi-supervised learning in a complex arm motor control task". En 2008 IEEE International Conference on Robotics and Biomimetics. IEEE, 2009. http://dx.doi.org/10.1109/robio.2009.4913257.
Texto completoHan, Jinpei, Xiao Gu y Benny Lo. "Semi-Supervised Contrastive Learning for Generalizable Motor Imagery EEG Classification". En 2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE, 2021. http://dx.doi.org/10.1109/bsn51625.2021.9507038.
Texto completoLotey, Taveena, Prateek Keserwani, Gaurav Wasnik y Partha Pratim Roy. "Cross-Session Motor Imagery EEG Classification using Self-Supervised Contrastive Learning". En 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956291.
Texto completoRovini, E., A. Moschetti, L. Fiorini, D. Esposito, C. Maremmani y F. Cavallo. "Wearable Sensors for Prodromal Motor Assessment of Parkinson’s Disease using Supervised Learning*". En 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856804.
Texto completoDharani, M. y M. Sivachitra. "Motor imagery signal classification using semi supervised and unsupervised extreme learning machines". En 2017 4th International Conference on Innovations in Information, Embedded and Communication Systems. IEEE, 2017. http://dx.doi.org/10.1109/iciiecs.2017.8276131.
Texto completoBamdadian, A., Cuntai Guan, Kai Keng Ang y Jianxin Xu. "Online semi-supervised learning with KL distance weighting for Motor Imagery-based BCI". En 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6346529.
Texto completoKewalramani, Rohit y A. Ram. "Estimation of Remaining Useful Life of Electric Motor using supervised deep learning methods". En 2019 IEEE Transportation Electrification Conference (ITEC-India). IEEE, 2019. http://dx.doi.org/10.1109/itec-india48457.2019.itecindia2019-197.
Texto completoPapageorgiou, T. D., W. A. Curtis, M. McHenry y S. M. LaConte. "Neurofeedback of two motor functions using supervised learning-based real-time functional magnetic resonance imaging". En 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5333703.
Texto completoRajapaksha, Nipuna, Shantha Jayasinghe, Hossein Enshaei y Nirman Jayarathne. "Supervised Machine Learning Algorithm Selection for Condition Monitoring of Induction Motors". En 2021 IEEE Southern Power Electronics Conference (SPEC). IEEE, 2021. http://dx.doi.org/10.1109/spec52827.2021.9709436.
Texto completo