Şahin, Erol


A Hopfield neural network with multi-compartmental activation
Akhmet, Marat (Springer Science and Business Media LLC, 2018-05-01)
The Hopfield network is a form of recurrent artificial neural network. To satisfy demands of artificial neural networks and brain activity, the networks are needed to be modified in different ways. Accordingly, it is the first time that, in our paper, a Hopfield neural network with piecewise constant argument of generalized type and constant delay is considered. To insert both types of the arguments, a multi-compartmental activation function is utilized. For the analysis of the problem, we have applied the ...
A shape descriptor based on circular Hidden Markov Model
Arica, N; Yarman Vural, Fatoş Tunay (2000-09-07)
Given the shape information of an object, can we find visually meaningful "n" objects in an image database, which is ranked from the most similar to the n(th) similar one? The answer to this question depends on the complexity of the images in the database and the complexity of the objects in the query.
A low-order nonlinear amplifier model with distributed delay terms
YÜZER, AHMET HAYRETTİN; Demir, Şimşek (The Scientific and Technological Research Council of Turkey, 2014-01-01)
In this paper, a novel behavioral modeling technique for the characterization of memory effects of amplifiers is proposed. This characterization utilizes asymmetric intermodulation distortion (IMD)components, which are the result of a 2-tone excitation of a nonlinear amplifier. These asymmetric IMD components are represented basically by a power series, where each term in the series has its own time delay term. These time delay terms successfully justify the presence of asymmetry in the intermodulation comp...
A Cartesian Based Mesh Generator with Body Fitted Boundary Layers
Özkan, Merve; Baran, Özgür Uğraş; Aksel, Mehmet Haluk (null; 2018-07-03)
A capacitated inventory model with a fixed ordering cost under stochastic demand
Özener, Okan Örsan; Güllü, Refik; Erkip, Nesim; Department of Industrial Engineering (2003)
Citation Formats
M. GULER and E. Şahin, “A BINARY-INPUT SUPERVISED NEURAL UNIT THAT FORMS INPUT DEPENDENT HIGHER-ORDER SYNAPTIC CORRELATIONS,” 1994, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55722.