On interpretation of Graffiti digits and characters for eBooks: Neural-fuzzy network and genetic algorithm approach

Leung, KF; Leung, FHF; Lam, HK; Ling, SH

On interpretation of Graffiti digits and characters for eBooks: Neural-fuzzy network and genetic algorithm approach

Leung, KF Leung, FHF Lam, HK Ling, SH

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Electronics, 2004, 51 (2), pp. 464 - 471
Issue Date:: 2004-04-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (597.6 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Leung, KF	en_US
dc.contributor.author	Leung, FHF	en_US
dc.contributor.author	Lam, HK	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.date.issued	2004-04-01	en_US
dc.identifier.citation	IEEE Transactions on Industrial Electronics, 2004, 51 (2), pp. 464 - 471	en_US
dc.identifier.issn	0278-0046	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15326
dc.description.abstract	This paper presents the rule optimization, tuning of the membership functions, and optimization of the number of fuzzy rules, of a neural-fuzzy network (NFN) using a genetic algorithm (GA). The objectives are achieved by training a proposed NFN with rule switches. The proposed NFN and GA are employed to interpret graffiti number inputs and commands for electronic books (eBooks).	en_US
dc.relation.ispartof	IEEE Transactions on Industrial Electronics	en_US
dc.relation.isbasedon	10.1109/TIE.2004.825285	en_US
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	On interpretation of Graffiti digits and characters for eBooks: Neural-fuzzy network and genetic algorithm approach	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	51	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
dc.location.activity	WOS:000220656700023	en_US
dc.location.activity	WOS:000291112200005
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	51	en_US

Abstract:

This paper presents the rule optimization, tuning of the membership functions, and optimization of the number of fuzzy rules, of a neural-fuzzy network (NFN) using a genetic algorithm (GA). The objectives are achieved by training a proposed NFN with rule switches. The proposed NFN and GA are employed to interpret graffiti number inputs and commands for electronic books (eBooks).

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/15326