title: Function approximation with spiked random networks.
abstract: This paper examines the function approximation properties of the "random neural-network model" or GNN. The output of the GNN can be computed from the firing probabilities of selected neurons. We consider a feedforward Bipolar GNN (BGNN) model which has both "positive and negative neurons" in the output layer, and prove that the BGNN is a universal function approximator. Specifically, for any f is an element of C([0, 1]s) and any epsilon>0, we show that there exists a feedforward BGNN which approximates f uniformly with error less than epsilon. We also show that after some appropriate clamping operation on its output, the feedforward GNN is also a universal function approximator.
authors.affiliation: School of Computer Science, University of Central Florida, Orlando, FL 32789, USA.
authors.forename: E
authors.initials: E
authors.lastname: Gelenbe
authors.forename: Z H
authors.initials: ZH
authors.lastname: Mao
authors.forename: Y D
authors.initials: YD
authors.lastname: Li