Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0925-2312(97)00030-1
DC FieldValue
dc.titleOn the solution of the parity problem by a single hidden layer feedforward neural network
dc.contributor.authorSetiono, R.
dc.date.accessioned2014-10-27T06:03:26Z
dc.date.available2014-10-27T06:03:26Z
dc.date.issued1997-09-01
dc.identifier.citationSetiono, R. (1997-09-01). On the solution of the parity problem by a single hidden layer feedforward neural network. Neurocomputing 16 (3) : 225-235. ScholarBank@NUS Repository. https://doi.org/10.1016/S0925-2312(97)00030-1
dc.identifier.issn09252312
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/99365
dc.description.abstractIt is known that the N-bit parity problem is solvable by a standard feedforward neural network having a single hidden layer consisting of (N/2) + 1 hidden units if N is even and (N + 1)/2 hidden units if N is odd. The network does not allow a direct connection between the input layer and the output layer and the transfer function used in all hidden units and the output unit is the usual sigmoidal function σ(x)-1/(1 + exp(-x)). We show that such a solution can be easily obtained by solving a system of linear equations.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S0925-2312(97)00030-1
dc.sourceScopus
dc.subjectFeedforward network
dc.subjectParity problem
dc.subjectSigmoid function
dc.typeArticle
dc.contributor.departmentINFORMATION SYSTEMS & COMPUTER SCIENCE
dc.description.doi10.1016/S0925-2312(97)00030-1
dc.description.sourcetitleNeurocomputing
dc.description.volume16
dc.description.issue3
dc.description.page225-235
dc.description.codenNRCGE
dc.identifier.isiutA1997XW40400004
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

24
checked on Nov 23, 2020

WEB OF SCIENCETM
Citations

23
checked on Nov 23, 2020

Page view(s)

60
checked on Nov 29, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.