Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4060
Title: Trajectory prediction of moving objects by means of neural networks
Authors: Aytaç, İsmail Sıtkı
Barışık, Hakan
Issue Date: 1997
Publisher: Izmir Institute of Technology
Abstract: Estimating 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.
Description: Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 1997
Includes bibliographical references (leaves: 103-105)
Text in English; Abstract: Turkish and English
viii, 105 leaves
URI: http://hdl.handle.net/11147/4060
Appears in Collections:Master Degree / Yüksek Lisans Tezleri

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