Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Neural networks, linear functions and neglected non-linearity

Curry, Bruce and Morgan, Peter Huw ORCID: 2003. Neural networks, linear functions and neglected non-linearity. Computational Management Science 1 (1) , pp. 15-29. 10.1007/s10287-003-0003-4

Full text not available from this repository.


The multiplicity of approximation theorems for Neural Networks do not relate to approximation of linear functions per se. The problem for the network is to construct a linear function by superpositions of non-linear activation functions such as the sigmoid function. This issue is important for applications of NNs in statistical tests for neglected nonlinearity, where it is common practice to include a linear function through skip-layer connections. Our theoretical analysis and evidence point in a similar direction, suggesting that the network can in fact provide linear approximations without additional ‘assistance’. Our paper suggests that skip-layer connections are unnecessary, and if employed could lead to misleading results.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords: Universal approximation; non-linear regression; network weights; hidden layers; skip-layer connections
Publisher: Springer
ISSN: 1619-697X
Last Modified: 21 Oct 2022 09:49

Citation Data

Actions (repository staff only)

Edit Item Edit Item