Adaptive neuron model-an architecture for the rapid learning of nonlinear topological transformations

A method and an apparatus 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 has been applied to the highly degenerative inverse kinematics pr...

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Bibliographic Details
Main Author TAWEL; RAOUL
Format Patent
LanguageEnglish
Published 06.12.1994
Edition5
Subjects
CALCULATING
CHAMBERS PROVIDED WITH MANIPULATION DEVICES
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HAND TOOLS
MANIPULATORS
PERFORMING OPERATIONS
PHYSICS
PORTABLE POWER-DRIVEN TOOLS
TRANSPORTING
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Summary:A method and an apparatus 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 has been applied to the highly degenerative 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 109 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 APPROX 10 mu sec.
Bibliography:Application Number: US19920937335