Paper data
Title:
Two-dimensional feed-forward functionally expanded neural network Author(s): Panagopoulos Spyros, University of Strathclyde Soraghan John, University of Strathclyde Page numbers in the proceedings: Volume I pp 329-332 Session: Nonlinear Signal and Systems / Adaptive Methods
Paper abstract
This paper is concerned with the development of a two-dimensional feed-forward functionally expanded neural network (2D FFENN) surface modeler. New nonlinear surface basis functions are proposed for the network's functional expansion. A network optimization technique based on an iterative function selection strategy is also described. Comparative simulation results for surface mappings generated by the 2D FFENN, Multi-layered Perceptron (MLP) and Radial Basis Function (RBF) architectures are presented.
Paper
|