Aerodynamic parameter estimation using aeroballistic data

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A recommended neural trip distributon model Tapkın, Serkan; Akyılmaz, M. Özdemir; Department of Civil Engineering (2004) In this dissertation, it is aimed to develop an approach for the trip distribution element which is one of the important phases of four-step travel demand modelling. The trip distribution problem using back-propagation artificial neural networks has been researched in a limited number of studies and, in a critically evaluated study it has been concluded that the artificial neural networks underperform when compared to the traditional models. The underperformance of back-propagation artificial neural network...
Temporal and spatial forecasting of the foF2 values up to twenty four hours in advance Tulunay, E; Ozkaptan, C; Tulunay, Yurdanur (2000-01-01) Radio waves of a wide range of frequencies from very low frequency (VLF) to high frequency (HF), (broadly 3 to 30 MHz) can be propagated to great distances via the ionosphere.
Neural network prediction of tsunami parameters in the aegean and Marmara Seas Erdurmaz, Muammer Sercan; Ergin, Ayşin; Department of Civil Engineering (2004) Tsunamis are characterized as shallow water waves, with long periods and wavelengths. They occur by a sudden water volume displacement. Earthquake is one of the main reasons of a tsunami development. Historical data for an observation period of 3500 years starting from 1500 B.C. indicates that approximately 100 tsunamis occurred in the seas neighboring Turkey. Historical earthquake and tsunami data were collected and used to develop two artificial neural network models to forecast tsunami characteristics fo...

Citation Formats

G. Mahmutyazıcıoğlu, “Aerodynamic parameter estimation using aeroballistic data,” Ph.D. - Doctoral Program, Middle East Technical University, 2000.