Title

Function Approximation With Spiked Random Networks

Keywords

Function approximation random neural networks; Spiked neural 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 ∈ C([0, 1] s) and any ∈ > 0, we show that there exists a feedforward BGNN which approximates f uniformly with error less than ∈. We also show that after some appropriate clamping operation on its output, the feedforward GNN is also a universal function approximator. © 1999 IEEE.

Publication Date

12-1-1999

Publication Title

IEEE Transactions on Neural Networks

Volume

10

Issue

1

Number of Pages

3-9

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/72.737488

Socpus ID

0032762126 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0032762126

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