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- W2103997642 abstract "We discuss the parametrized self-organizing maps (PSOM) as a learning method for rapidly creating high-dimensional, continuous mappings. By making use of available topological information the PSOM shows excellent generalization capabilities from a small set of training data. The PSOM provides, as an important generalization, a flexibly usable continuous associate memory. Task specifications for redundant manipulators often leave the problem of picking one action from a subspace of possible alternatives. The PSOM approach offers a flexible and compact form to select various constraint and target functions previously associated. We present application results for learning several kinematic relations of a hydraulic robot finger in a single PSOM module. Based on only 27 data points, the PSOM learns the inverse kinematic with a mean positioning accuracy of 1% of the entire workspace. Also, the PSOM learns various ways to resolve the redundancy problem for positioning a 4-DOF manipulator." @default.
- W2103997642 created "2016-06-24" @default.
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- W2103997642 date "2002-11-27" @default.
- W2103997642 modified "2023-09-26" @default.
- W2103997642 title "PSOM network: learning with few examples" @default.
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- W2103997642 doi "https://doi.org/10.1109/robot.1998.680619" @default.
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