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 Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
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 |
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Conference Date | 2014/12/6(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Neurocomputing (NC) |
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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 |