Klaus P. - Academia.edu (original) (raw)

Papers by Klaus P.

Eur J Inorg Chem, 1958

I-MefhjiI-cvclohexanon-(2)-essigsaure-(I)-athylesfer (I V b) : 16 g I Va, 60 ccm Athanol, I20 ccm... more I-MefhjiI-cvclohexanon-(2)-essigsaure-(I)-athylesfer (I V b) : 16 g I Va, 60 ccm Athanol, I20 ccm Benzol und 1 ccm konz. Schwefelstiure werden 5 Stdn. unter RuckfluB erhitzt. Nach dem Abkuhlen wird die organische Phase rnit 10-proz. Natriurncarbonatlasung neutral gewaschen, anschlieBend mit Wass-,r geschuttelt und uber Na2S04 getrocknet. Das Benzol wird abdestilliert und der Ruckstand fraktioniert. Ausb. 12.5 g (65 % d. Th.), Sdp.o.2 103 bis 104".

Eur J Inorg Chem, 1958

Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468)

Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop

Lecture Notes in Computer Science, 1997

Many tasks in robotics are difficult to tackle with explicit models, based on “first principles”.... more Many tasks in robotics are difficult to tackle with explicit models, based on “first principles”. Consequently, neural networks with their inherent learning ability can offer feasible alternatives to more traditional approaches. Focusing on neural network approaches that have been derived from the Self-Organizing Map, we review several examples, how such networks can contribute to the solution of typical tasks in robotics, such as map building, object recognition and, in particular, the coordination of multi-joint movements. We argue, that a frequent common structure is a “continuous associative memory” for the flexible representation of continuous relations between degrees of freedom, and show how such representation can be obtained with a parametrized Self-organizing Map.

Lecture Notes in Computer Science, 1997