NASA NTRS · 19930053016
Nonlinear functional approximation with networks using adaptive neurons
Abstract
A novel mathematical framework for the rapid learning of nonlinear mappings and topological transformations is presented. It is based on allowing the neuron's parameters to adapt as a function of learning. This fully recurrent adaptive neuron model (ANM) has been successfully applied to complex nonlinear function approximation problems such as the highly degenerate inverse kinematics problem in robotics.
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Tawel, Raoul. 1992-01-01. Nonlinear functional approximation with networks using adaptive neurons. https://ntrs.nasa.gov/citations/19930053016
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