Title

Function approximation with spiked random networks

Authors

Authors

E. Gelenbe; Z. H. Mao;Y. D. Li

Comments

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Abbreviated Journal Title

IEEE Trans. Neural Netw.

Keywords

function approximation random neural networks; spiked neural networks; NEURAL NETWORKS; COMPRESSION; Computer Science, Artificial Intelligence; Computer Science, Hardware &; Architecture; Computer Science, Theory & Methods; Engineering, ; Electrical & Electronic

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 I 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.

Journal Title

Ieee Transactions on Neural Networks

Volume

10

Issue/Number

1

Publication Date

1-1-1999

Document Type

Article

Language

English

First Page

3

Last Page

9

WOS Identifier

WOS:000077977800002

ISSN

1045-9227

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