Neuro-fuzzy Learning Applied to Improve the Trajectory Reconstruction Problem

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dc.contributor.author Concha, OP
dc.contributor.author Garcia, J
dc.contributor.author Molina, JM
dc.contributor.editor Masoud Mohammadian
dc.date.accessioned 2012-02-02T11:08:03Z
dc.date.issued 2006-01
dc.identifier.citation International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 2006) Jointly with International Conference on Intelligent Agents Web Technologies and International Commerce (IAWTIC 2006), 2006, pp. 1 - 6
dc.identifier.isbn 0-7695-2731-0
dc.identifier.other E1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/16255
dc.description.abstract This paper presents the application of a neuro-fuzzy learning approach to classify Air Traffic Control (ATC) trajectory segments from recorded opportunity traffic. This method learns a fuzzy system using neural-network theory to determine its parameters (fuzzy sets and fuzzy rules) by processing data samples. The problem is prepared for analysing the Markovchain probabilities estimated by an Interacting Multiple Model (IMM) tracking filter operating forward and backward over available data. The performance of this data-driven classification system is compared with a more conventional approach based on transition detection on simulated and real data of representative situations. The problem's formulation for this application enabled an accurate classification of manoeuvring segments and the derivation of rules that explain the relation between input attributes and motion categories used to describe the recorded data.
dc.publisher IEEE
dc.relation.isbasedon 10.1109/CIMCA.2006.157
dc.title Neuro-fuzzy Learning Applied to Improve the Trajectory Reconstruction Problem
dc.type Conference Proceeding
dc.parent International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 2006) Jointly with International Conference on Intelligent Agents Web Technologies and International Commerce (IAWTIC 2006)
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 1 en_US
dc.identifier.endpage 6 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.conference International Conference on Intelligent Agents Web Technologies and International Commerce
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 104828
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom International Conference on Intelligent Agents Web Technologies and International Commerce en_US
dc.date.activity 20061129 en_US
dc.date.activity 2006-11-29
dc.location.activity Sydney, NSW, Australia en_US
dc.description.keywords Markov processes , air traffic control , fuzzy neural nets , fuzzy set theory , learning (artificial intelligence) , neurocontrollers , position control en_US
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
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)


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