Rapid Learning in Robotics - Jorg Walter Part 4 pdf
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 avail- able training data set and adding a small amount ... cooperative learning in order to increase speed and ro- bustness of learning, studied e.g. in Walter, Martinetz, and Schulten (1991) and compared to the so-called Neural-Gas Networ...
Ngày tải lên: 10/08/2014, 02:20
Rapid Learning in Robotics - Jorg Walter Part 1 docx
... walter@ techfak.uni-bielefeld.de Url: http://www.techfak.uni-bielefeld.de/ walter/ c 1997 for hard copy publishing: Cuvillier Verlag Nonnenstieg 8, D-37075 Göttingen, Germany, Fax: +4 9-5 5 1-5 47 2 4- 2 1 iv vi ... 106 8.1 [a–d] Kinematic workspace of the TUM robot finger . . . . . 108 8.2 [a–e] Training and testing of the finger kinematics PSOM . . 110 Jörg A. Walter Rapid Lear...
Ngày tải lên: 10/08/2014, 02:20
Rapid Learning in Robotics - Jorg Walter Part 2 ppsx
... comply to extra constraints. Chapter 9 turns to the next higher level of one-shot learning. Here the learning of prototypical mappings is used to rapidly adapt a learning sys- tem to new context ... investment learning stage, since effort is invested, to train the system for the second, the one-shot learning phase. Observing the context, the system can now adapt most rapidly by “mixi...
Ngày tải lên: 10/08/2014, 02:20
Rapid Learning in Robotics - Jorg Walter Part 3 ppsx
... training 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 ... by the so-called “catastrophic inter- ference”, see “on-line learning below. Batch versus Incremental Learning: Calculating the network weight up- dates under consideration of all t...
Ngày tải lên: 10/08/2014, 02:20
Rapid Learning in Robotics - Jorg Walter Part 6 pot
... continuous manifold in . However, in certain cases input-output mappings are non-continuous. The particular manifold shape in conjunction with the associative completion and its op- tional partial ... growing “remoteness” to the trained mapping area. This property limits the extrapolation abilities of the PSOM, depending on the particular distribution of training data. The beginning in...
Ngày tải lên: 10/08/2014, 02:20
Rapid Learning in Robotics - Jorg Walter Part 8 ppt
... augmenting 729 joint angle vectors on a rectangular 3 3 3 3 3 3 grid in joint angle space with the missing – 112 Application Examples in the Robotics Domain -4 0 -3 0 -2 0 -1 0 0 10 20 30 40 -4 0 -3 0 -2 0 -1 0 0 10 20 3 0 90 100 110 120 130 140 150 160 x y z r ... Kinematics Mapping 113 -4 0 -3 0 -2 0 -1 0 0 10 20 30 40 -4 0 -3 0 -2 0...
Ngày tải lên: 10/08/2014, 02:20
Rapid Learning in Robotics - Jorg Walter Part 9 docx
... mm 0. 041 3 3 3 C-PSOM 11 mm 0.027 4 4 4 PSOM 2 .4 mm 0.0061 4 4 4 C-PSOM 1.7 mm 0.0 042 5 5 5 PSOM 0.11 mm 0.00027 5 5 5 C-PSOM 0.091 mm 0.00023 3 3 3 L-PSOM of 4 4 4 6.7 mm 0. 041 3 3 3 L-PSOM ... mappings are smooth in certain domains, but non- continuous in others. Then, different types of learning experts, like PSOMs, Meta-PSOMs, LLMs, RBF and others can be chosen....
Ngày tải lên: 10/08/2014, 02:20
Rapid Learning in Robotics - Jorg Walter Part 10 pps
... efficient learning modules for the continuous and smooth mapping domain, the “mixture-of-expert” scheme is superior in managing mapping domains which require non-continuous or non-smooth interfaces. ... random lo- cations (from within the range of the training set) seen in 10 different 138 “Mixture-of-Expertise” or “Investment Learning camera setups, from within the square grid of the...
Ngày tải lên: 10/08/2014, 02:20
Rapid Learning in Robotics - Jorg Walter Part 11 pps
... Report SFB360-TR-9 6-3 , Universität Bielefeld, D-33615 Bielefeld. Walter, J., H. Ritter, and K. Schulten (1990, June). Non-linear predic- tion with self-organizing maps. In Int. Joint Conf. on ... Proc. Int. Conf. on Artificial Neural Networks (ICANN-91), Espoo, Finland, pp. 40 3 40 8. North-Holland, Amsterdam. Fritzke, B. (1995). Incremenal learning of local linear mappings. In Pro...
Ngày tải lên: 10/08/2014, 02:20
rapid learning in robotics jorg walter pot
... training 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 ... by the so-called “catastrophic inter- ference”, see “on-line learning below. Batch versus Incremental Learning: Calculating the network weight up- dates under consideration of all t...
Ngày tải lên: 27/06/2014, 18:20