Lateral pressure on rigid retaining walls : a neural network approach

Yıldız, Ersan


Neural networks with piecewise constant argument and impact activation
Yılmaz, Enes; Akhmet, Marat; Department of Scientific Computing (2011)
This dissertation addresses the new models in mathematical neuroscience: artificial neural networks, which have many similarities with the structure of human brain and the functions of cells by electronic circuits. The networks have been investigated due to their extensive applications in classification of patterns, associative memories, image processing, artificial intelligence, signal processing and optimization problems. These applications depend crucially on the dynamical behaviors of the networks. In t...
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.
Neuro controlled fractal inference networks and their application to grasping with multifingered robot hands
Nazlıbilek, Sedat; Erkmen, Aydan Müşerref; Department of Electrical and Electronics Engineering (1993)
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...
Texture classification and retrieval using random neural network model
Teke, Alper; Atalay, Mehmet Volkan; Department of Computer Engineering (2003)
Texture is one of the most important characteristics used in computer vision and image processing applications. In this thesis, a new texture classification and retrieval method is proposed for texture analysis applications. The technique makes use of the random neural network model and it is supervised. The main aim is to represent textures with parameters which are the random neural network weights and classify and retrieve textures using this texture definition. The network has neurons that correspond to...
Citation Formats
E. Yıldız, “Lateral pressure on rigid retaining walls : a neural network approach,” M.S. - Master of Science, Middle East Technical University, 2003.