Phys chemchem phys,2013 jorg geli
Phys chemchem phys,2013 jorg geli
... Phys Lett., 2011, 502, 187; (c) T B Tai and M T Nguyen, J Phys Chem A, 2011, 115, 9993 J Wang, L Ma, J Zhao and G Wang, J Phys. : Condens Matter, 2008, 20, 335223 V Kumar and Y Kawazoe, Appl Phys ... Martin, J Chem Phys. , 1989, 90, 2848 48 M J Frisch, et al., Gaussian 03, Revision D.02, Gaussian, Inc., Wallingford, CT, 2004 49 A D Becke, J Chem Phys. , 1993, 98, 5648 Phys Chem Che...
rapid learning in robotics jorg walter pot
... position More training data: Over-fitting can be avoided when sufficient training points are available, e.g by learning on-line Duplicating the available training data set and adding a small amount ... ordering and point out several distinguishable axes: Supervised versus Unsupervised and Reinforcement Learning: In supervised learning paradigm, the training input signal is given with a p...
Rapid Learning in Robotics - Jorg Walter Part 1 docx
... 9.7 11 1 11 2 11 3 11 4 11 6 11 8 12 1 12 1 12 3 Context dependent mapping tasks 12 6 The investment learning phase 12 7 The one-shot adaptation phase 12 8 ... http://www.techfak.uni-bielefeld.de/ walter/ c 19 97 for hard copy publishing: Cuvillier Verlag Nonnenstieg 8, D-37075 Göttingen, Germany, Fax: +4 9-5 5 1- 5 472 4-2 1 Jörg A Walter Rapid Learning in R...
Rapid Learning in Robotics - Jorg Walter Part 2 ppsx
... engineering, control, and communication sciences The time for gathering training data becomes a major issue This includes also the time for preparing the learning set-up In principle, the learning ... PSOM learning time reduces to an immediate construction This feature is of particular interest in the domain of robotics: as already pointed out, here the cost of gathering the training...
Rapid Learning in Robotics - Jorg Walter Part 3 ppsx
... examples in a stochastic sequence Iterative learning is usually more efficient, particularly w.r.t memory requirements Off-line versus On-line Learning and Interferences: Off-line learning allows ... Perceptron The learning algorithm described a way of iteratively changing the weights J Walter Rapid Learning in Robotics 23 24 Artificial Neural Networks x1 x2 wi1 wi2 x3 x1 wi3...
Rapid Learning in Robotics - Jorg Walter Part 4 pdf
... position More training data: Over-fitting can be avoided when sufficient training points are available, e.g by learning on-line Duplicating the available training data set and adding a small amount ... in the embedding space X at the left side Figure 4. 2: The mapping a A Specifying for each training vector a node location introduces a topological order between the training points a : trai...
Rapid Learning in Robotics - Jorg Walter Part 6 pot
... training data The beginning in- folding of the map, e.g seen at the lower left corner in Fig 5.8 demonstrates further that M shows multiple solutions (Eq 4.4) for finding a best-match in X34 In ... compared with one single interpolation polynomial in a selected node sub-grid, as described For m = the bi-cubic, so-called tensor-product spline is usually computed by row-wise spline inte...
Rapid Learning in Robotics - Jorg Walter Part 8 ppt
... Domain z 160 150 140 130 120 110 100 90 40 30 20 10 x -1 0 -2 0 -3 0 -4 0 -4 0 -3 0 -2 0 -1 0 y r 10 20 30 θ Figure 8. 4: The 27 training data vectors for the Back-propagation networks: (left) in the input ... 8. 2 The Inverse D Robot Kinematics Mapping z 113 wa 160 150 a 140 130 120 110 100 90 40 30 20 10 x -1 0 -2 0 -3 0 -4 0 r s2 -4 0 -3 0 -2...
Rapid Learning in Robotics - Jorg Walter Part 9 docx
... However, in the case n = both sampling schemes have equidistant node-spacing, but the Chebyshev-spacing approach contracts the marginal sampling points inside the working interval Since the vicinity ... “Investment Learning or “Mixture-of-Expertise” Architecture Input Context Gating Network Σ Task Variables T-Box Expert Output T-Box Expert T-Box Expert T-Box Expert N ‘‘Mixture-of-Exper ts’...
Rapid Learning in Robotics - Jorg Walter Part 10 pps
... as the T-B OX /M ETA -B OX approach are very efficient learning modules for the continuous and smooth mapping domain, the “mixture-of-expert” scheme is superior in managing mapping domains which ... averaged over 100 random locations (from within the range of the training set) seen in 10 different 138 “Mixture-of-Expertise” or “Investment Learning camera setups, from within the 3 squ...
Rapid Learning in Robotics - Jorg Walter Part 11 pps
... Technical Report SFB360-TR-9 6-3 , Universität Bielefeld, D-33615 Bielefeld Walter, J., H Ritter, and K Schulten (1990, June) Non-linear prediction with self-organizing maps In Int Joint Conf on Neural ... Notes in Computer Science 111 2, pp 157–164 Springer Walter, J and H Ritter (1996b) Investment learning with hierarchical PSOM In D Touretzky, M Mozer, and M Hasselmo (Eds.), A...
99 j phys cond matt 24(2012) 266007
... F E, McNiff E J and Foner S Jr 1999 J Magn Magn Mater 196–197 591 [19] Kodama R H 1999 J Magn Magn Mater 200 359 [20] Kodama R H, Berkowitz A E, McNiff E J and Foner S Jr 1996 Phys Rev Lett 77 ... Please note that terms and conditions apply IOP PUBLISHING JOURNAL OF PHYSICS: CONDENSED MATTER J Phys. : Condens Matter 24 (2012) 266007 (6pp) doi:10.1088/0953-8984/24/26 /266007 E...
J phys condens matter 2008 20 335223 mngen china
... IOP PUBLISHING JOURNAL OF PHYSICS: CONDENSED MATTER J Phys. : Condens Matter 20 (200 8) 335223 (8pp) doi:10.1088/0953-8984 /20/ 33 /335223 Structural growth sequences and electronic ... Wang J and Han J G 200 5 J Chem Phys 123 244303 [20] Wang J and Han J G 200 6 J Phys Chem 110 12670 [21] Zhang X, Li G and Gao Z 200 1 Rapid Commun Mass Spectrom 15 1573 [22]...
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