Combinational neural logic system and its industrial application on hypoglycemia monitoring system

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dc.contributor.author San, PP
dc.contributor.author Ling, SH
dc.contributor.author Nguyen, HT
dc.date.accessioned 2014-04-03T02:23:32Z
dc.date.issued 2013
dc.identifier.citation Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013, 2013, pp. 947 - 952
dc.identifier.isbn 9781467363211
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/23607
dc.description.abstract In this paper, a combinational neural logic network (NLN) with the neural-Logic-AND, -OR and -NOT gates is applied on the development of non-invasive hypoglycemia monitoring system. It is an alarm system which measured physiological parameters of electrocardiogram (ECG) signal and determine the onset of hypoglycemia by use of proposed NLN. Due to different nature of application, conventional neural networks (NNs) with common structure may not always guarantee the optimal solution. Based on knowledge of application, the proposed NLN is designed systematically in order to incorporate the characteristics of application into the structure of proposed network. The parameter of the proposed NLN will be trained by hybrid particle swarm optimization with wavelet mutation (HPSOWM). The proposed NLN will be practically analyzed using real data sets collected from 15 children (569 data sets) with Type 1 diabetes at the Department of Health, Government of Western Australia. By using the proposed method, the detection performance is enhanced. Compared with other conventional NNs, the proposed NLN gives better performance in terms of sensitivity and specificity. © 2013 IEEE.
dc.relation.isbasedon 10.1109/ICIEA.2013.6566503
dc.title Combinational neural logic system and its industrial application on hypoglycemia monitoring system
dc.type Conference Proceeding
dc.parent Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013
dc.journal.number en_US
dc.publocation US en_US
dc.publocation US
dc.identifier.startpage 947 en_US
dc.identifier.endpage 952 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.conference ICIEA 2013
dc.for 080108 Neural, Evolutionary and Fuzzy Computation
dc.personcode 840115
dc.personcode 112356
dc.personcode 106694
dc.percentage 100 en_US
dc.classification.name Neural, Evolutionary and Fuzzy Computation en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom ICIEA 2013 en_US
dc.date.activity 20130619 en_US
dc.date.activity 2013-06-19
dc.location.activity Melbourne, Australia en_US
dc.location.activity Melbourne, Australia
pubs.embargo.period Not known
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 Elec, Mech and Mechatronic Systems
pubs.organisational-group /University of Technology Sydney/Strength - Health Technologies
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true


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