Nonlinear functional approximation with networks using adaptive neuronsA 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.
Document ID
19930053016
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Tawel, Raoul (JPL Pasadena, CA, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1992
Publication Information
Publication: In: IJCNN - International Joint Conference on Neural Networks, Baltimore, MD, June 7-11, 1992, Proceedings. Vol. 3 (A93-37001 14-63)
Publisher: Institute of Electrical and Electronics Engineers, Inc.