Title

Approximation By Random Networks With Bounded Number Of Layers

Abstract

This paper discusses the function approximation properties of the 'Gelenbe' random neural network (GNN). We use an extension of the basic model; the bipolar GNN (BGNN). We limit the networks to being feedforward and consider the case where the number of hidden layers does not exceed the number of input layers. We show that the feedforward BGNN with s hidden layers (total of s + 2 layers) can uniformly approximate continuous functions of s variables.

Publication Date

12-1-1999

Publication Title

Neural Networks for Signal Processing - Proceedings of the IEEE Workshop

Number of Pages

166-175

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

0033312854 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0033312854

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