| タイトル | Adaptive Neuron Model: An architecture for the rapid learning of nonlinear topological transformations |
| 本文（外部サイト） | http://hdl.handle.net/2060/19950012325 |
| 著者（英） | Tawel, Raoul |
| 著者所属（英） | NASA Pasadena Office; Jet Propulsion Lab., California Inst. of Tech. |
| 発行日 | 1994-12-06 |
| 言語 | eng |
| 内容記述 | A method for the rapid learning of nonlinear mappings and topological transformations using a dynamically reconfigurable artificial neural network is presented. This fully-recurrent Adaptive Neuron Model (ANM) network was applied to the highly degenerate inverse kinematics problem in robotics, and its performance evaluation is bench-marked. Once trained, the resulting neuromorphic architecture was implemented in custom analog neural network hardware and the parameters capturing the functional transformation downloaded onto the system. This neuroprocessor, capable of 10(exp 9) ops/sec, was interfaced directly to a three degree of freedom Heathkit robotic manipulator. Calculation of the hardware feed-forward pass for this mapping was benchmarked at approximately 10 microsec. |
| NASA分類 | CYBERNETICS |
| レポートNO | 95N18740 |
| 権利 | No Copyright |
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