Abstract
In this paper, we investigate a strategy for motor learning and propose a learning control model that integrates internal model learning and the viscoelastic adjustment of the human arm. In this model, the value of the viscoelasticity is modulated by the values of a control error and a predictive error. Consequently, the adjustment mechanism eliminates the weak point that for conventional learning control models desired movements cannot be realized at the beginning of learning, by gradually shifting from relatively higher viscoelasticity to lower viscoelasticity through learning.
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© 2001 Springer-Verlag Berlin Heidelberg
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Katayama, M. (2001). A Neural Control Model Using Predictive Adjustment Mechanism of Viscoelastic Property of the Human Arm. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_134
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DOI: https://doi.org/10.1007/3-540-44668-0_134
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