Presentation 2014-12-13
Forward-Propagation Learning Rule to Acquire the Inverse Dynamics Model of an Arm with Coulomb's Friction
Yu KIYOSAWA, Takahiro KAGAWA, Yoji UNO,
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Abstract(in English) A forward-propagation learning rule has been used to acquire inverse models of controlled objects in a neural network as a feedforward controller. However, an approximate inverse model must be acquired before learning. We have proposed to combine a feedback controller with the forward-propagation learning rule. From randomized initial weights, the realized trajectory can converge to the desired trajectory by a few iterations of this learning scheme. In this report, our learning scheme was applied to acquire inverse dynamics models of a two-link arm with coulomb's friction and the efficacy was confirmed by computer simulation.
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Keyword(in English) Forward-propagation learning rule / inverse model / neural network / feedback controller / coulomb's friction
Paper # NC2014-49
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Committee NC
Conference Date 2014/12/6(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Forward-Propagation Learning Rule to Acquire the Inverse Dynamics Model of an Arm with Coulomb's Friction
Sub Title (in English)
Keyword(1) Forward-propagation learning rule
Keyword(2) inverse model
Keyword(3) neural network
Keyword(4) feedback controller
Keyword(5) coulomb's friction
1st Author's Name Yu KIYOSAWA
1st Author's Affiliation Graduate School and School of Engineering, Nagoya University()
2nd Author's Name Takahiro KAGAWA
2nd Author's Affiliation Graduate School and School of Engineering, Nagoya University
3rd Author's Name Yoji UNO
3rd Author's Affiliation Graduate School and School of Engineering, Nagoya University
Date 2014-12-13
Paper # NC2014-49
Volume (vol) vol.114
Number (no) 362
Page pp.pp.-
#Pages 6
Date of Issue