최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Robotics and autonomous systems, v.110, 2018년, pp.33 - 43
Duran, Angel J. (Corresponding author.) , del Pobil, Angel P.
Abstract The estimation of the internal model of a robotic system results from the interaction of its morphology, sensors and actuators, with a particular environment. Model learning techniques, based on supervised machine learning, are widespread for determining the internal model. An important li...
Science Bongard 314 5802 1118 2006 10.1126/science.1133687 Resilient machines through continuous self-modeling
Vaughan 298 2006 From Animals to Animats, vol. 9 Use your illusion: Sensorimotor self-simulation allows complex agents to plan with incomplete self-knowledge
Robot. Auton. Syst. Sigaud 59 12 1115 2011 10.1016/j.robot.2011.07.006 On-line regression algorithms for learning mechanical models of robots: A survey
Isidori 1 2003 Robust Autonomous Guidance Fundamentals of internal-model-based control theory
Cognit. Process. Nguyen-Tuong 12 4 319 2011 10.1007/s10339-011-0404-1 Model learning for robot control: a survey
Siciliano 2008 Robotics: Modelling, Planning and Control
Science Pfeifer 318 November 1088 2007 10.1126/science.1145803 Self-organization, embodiment, and biologically inspired robotics
Hudson 2000 IEEE Press Series in Biomedical Engineering Neural networks and artificial intelligence for biomedical engineering
IEEE Robot. Autom. Mag. Bonsignorio 22 3 32 2015 10.1109/MRA.2015.2452073 Toward replicable and measurable robotics research [from the guest editors]
Robot. Auton. Syst. Felip 61 3 283 2013 10.1016/j.robot.2012.11.010 Manipulation primitives: A paradigm for abstraction and execution of grasping and manipulation tasks
IEEE Syst. J. Khaitan 9 2 350 2015 10.1109/JSYST.2014.2322503 Design techniques and applications of cyberphysical systems: A survey
A.P. del Pobil, Robots as cyberphysical systems: The challenges ahead, in: Keynote Speech at the 12th ACM International Conference on Ubiquitous Information Management and Communication, Langkawi, Malaysia, 2018.
Hermann 3928 2016 System Sciences (HICSS), 2016 49th Hawaii International Conference on Design principles for industrie 4.0 scenarios
IEEE Access Wan 4 2797 2016 Cloud robotics: Current status and open issues
IEEE Trans. Autom. Sci. Eng. Kehoe 12 2 398 2015 10.1109/TASE.2014.2376492 A survey of research on cloud robotics and automation
IEEE Robot. Autom. Mag. Waibel 18 2 69 2011 10.1109/MRA.2011.941632 Roboearth
IEEE Access L’Heureux 5 7776 2017 10.1109/ACCESS.2017.2696365 Machine learning with big data: Challenges and approaches
IEEE Trans. Robot. Autom. Rucci 15 1 96 1999 10.1109/70.744606 Adaptation of orienting behavior: from the barn owl to a robotic system
Robot. Auton. Syst. Rucci 30 1 181 2000 10.1016/S0921-8890(99)00071-8 Robust localization of auditory and visual targets in a robotic barn owl
Adv. Robot. Rucci 21 10 1115 2007 10.1163/156855307781389428 Integrating robotics and neuroscience: Brains for robots, bodies for brains
Robot. Auton. Syst. Antonelli 71 13 2015 10.1016/j.robot.2014.11.018 Learning the visual-oculomotor transformation: Effects on saccade control and space representation
Kawato 365 1990 Advanced Neural Computers Feedback-error-learning neural network for supervised motor learning
Rahimi 1177 2007 Advances in Neural Information Processing Systems Random features for large-scale kernel machines
Neural Netw. Gijsberts 41 59 2013 10.1016/j.neunet.2012.08.011 Real-time model learning using incremental sparse spectrum gaussian process regression
J. Statist. Plann. Inference Kohler 139 4 1286 2009 10.1016/j.jspi.2008.07.012 Optimal global rates of convergence for nonparametric regression with unbounded data
Neural Netw. Møller 6 525 1993 10.1016/S0893-6080(05)80056-5 A scaled conjugate gradient algorithm for fast supervised learning supervised learning
ICML Unsupervised Transf. Learn. Baldi 37 2012 Autoencoders, unsupervised learning, and deep architectures
Icml Rifai 85 1 833 2011 Contractive auto-encoders : Explicit invariance during feature extraction
Science Holden 313 July 504 2006 Reducing the dimensionality of data with neural networks
CS294A Lecture Notes Ng 72 1 2011 Sparse autoencoder
Int. J. Mach. Learn. Cybern. Meng 8 5 1719 2017 10.1007/s13042-016-0550-y Research on denoising sparse autoencoder
Adv. Neural Inf. Process. Syst. Bengio 19 1 153 2007 Greedy layer-wise training of deep networks
Hastie 2009 The Elements of Statistical Learning: Data Mining, Inference, and Prediction
J. Amer. Statist. Assoc. Ye 93 441 120 1998 10.1080/01621459.1998.10474094 On measuring and correcting the effects of data mining and model selection
Neural Netw. Anders 12 2 309 1999 10.1016/S0893-6080(98)00117-8 Model selection in neural networks
J. Amer. Statist. Assoc. Kadane 99 465 279 2004 10.1198/016214504000000269 Methods and criteria for model selection
Burnham vol. 172 488 2002 Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
저자가 공개 리포지터리에 출판본, post-print, 또는 pre-print를 셀프 아카이빙 하여 자유로운 이용이 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.