Neuro-fuzzy Learning Applied to Improve the Trajectory Reconstruction Problem

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dc.contributor.author Concha, Oscar en_US
dc.contributor.author Garcia, Jesus en_US
dc.contributor.author Molina, Jose en_US
dc.contributor.editor Masoud Mohammadian en_US
dc.date.accessioned 2012-02-02T11:08:03Z
dc.date.available 2012-02-02T11:08:03Z
dc.date.issued 2006 en_US
dc.identifier 2009006241 en_US
dc.identifier.citation Concha Oscar, Garcia Jesus, and Molina Jose 2006, 'Neuro-fuzzy Learning Applied to Improve the Trajectory Reconstruction Problem', , IEEE, USA, , pp. 1-6. en_US
dc.identifier.issn 0-7695-2731-0 en_US
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. en_US
dc.language English en_US
dc.publisher IEEE en_US
dc.relation.isbasedon http://dx.doi.org/10.1109/CIMCA.2006.157 en_US
dc.title Neuro-fuzzy Learning Applied to Improve the Trajectory Reconstruction Problem en_US
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) en_US
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.for 080100 en_US
dc.personcode 104828 en_US
dc.personcode 0000036047 en_US
dc.personcode 0000036042 en_US
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.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


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