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Paper data
Two-dimensional feed-forward functionally expanded neural network

Panagopoulos Spyros, University of Strathclyde
Soraghan John, University of Strathclyde

Page numbers in the proceedings:
Volume I pp 329-332

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.

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