Abstract
In earlier papers, we have proposed the feedback-error-learning of inverse dynamics model of the musculoskeletal system as heterosynaptic learning scheme in the cerebrocerebellum and the parvocellular part of the red nucleus system, and the iterative learning in the parietal association cortex. In this paper, we applied hierarchical arrangement of these two neural network models to learning trajectory control of an industrial robotic manipulator. We found that the hierarchical arrangement of the cerebellar and cerebral neural networks not only increased control stability but also dramatically improved accuracy of control and reduced required learning time.
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© 1989 Springer-Verlag New York Inc.
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Kawato, M., Isobe, M., Suzuki, R. (1989). Hierarchical Learning of Voluntary Movement by Cerebellum and Sensory Association Cortex. In: Arbib, M.A., Amari, Si. (eds) Dynamic Interactions in Neural Networks: Models and Data. Research Notes in Neural Computing, vol 1. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4536-0_12
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DOI: https://doi.org/10.1007/978-1-4612-4536-0_12
Publisher Name: Springer, New York, NY
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