Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4060
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dc.contributor.advisorAytaç, İsmail Sıtkıen
dc.contributor.authorBarışık, Hakan-
dc.date.accessioned2014-07-22T13:53:05Z-
dc.date.available2014-07-22T13:53:05Z-
dc.date.issued1997en
dc.identifier.urihttp://hdl.handle.net/11147/4060-
dc.descriptionThesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 1997en
dc.descriptionIncludes bibliographical references (leaves: 103-105)en
dc.descriptionText in English; Abstract: Turkish and Englishen
dc.descriptionviii, 105 leavesen
dc.description.abstractEstimating the three-dimensional motion of an object from a sequence of object positions and orientation is of significant importance in variety of applications in control and robotics. For instance, autonomous navigation, manipulation, servo, tracking, planning and surveillance needs prediction of motion parameters. Although "motion estimation" is an old problem (the formulations date back to the beginning of the century), only recently scientists have provided with the tools from nonlinear system estimation theory to solve this problem eural Networks are the ones which have recently been used in many nonlinear dynamic system parameter estimation context. The approximating ability of the neural network is used to identifY the relation between system variables and parameters of a dynamic system. The position, velocity and acceleration of the object are estimated by several neural networks using the II most recent measurements of the object coordinates as input to the system Several neural network topologies with different configurations are introduced and utilized in the solution of the problem. Training schemes for each configuration are given in detail. Simulation results for prediction of motion having different characteristics via different architectures with alternative configurations are presented comparatively.en
dc.language.isoenen_US
dc.publisherIzmir Institute of Technologyen
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject.lccQA76.87 .B37 1997en
dc.subject.lcshNeural networks (Computer science)en
dc.titleTrajectory prediction of moving objects by means of neural networksen_US
dc.typeMaster Thesisen_US
dc.departmentIzmir Institute of Technology. Computer Engineeringen
dc.relation.publicationcategoryTezen_US
item.fulltextWith Fulltext-
item.openairetypeMaster Thesis-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
Appears in Collections:Master Degree / Yüksek Lisans Tezleri
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