Surface Approximation using the Multi-Level and Interpolation 2d FFENN

S. Panagopoulos and J.J. Soraghan (UK)


Neural networks, radar, sea clutter, surface modeling.


In this paper, we present the development of a two dimensional feed-forward functionally expanded neural network (2D FFENN) surface modeler. Two application models of the generic design are also proposed. The scope of this work is the development of a two-dimensional system able to produce surface data mappings. The main application area of interest is sea surface modeling and target detection by sea clutter suppression. New nonlinear multi-level 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. Results for surface mappings generated by the multi-level 2D FFENN and interpolation 2D FFENN designs are presented.

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