Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Non-uniform cellular finite automata neural networks
Download
035450.pdf
Date
1994
Author
Bakırkaya, Sedat
Metadata
Show full item record
Item Usage Stats
90
views
0
downloads
Cite This
URI
https://hdl.handle.net/11511/11654
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Non-uniform cellular automata with gaussian potential function units
Yıldırım, Dursun Gökhan; Güler, Marifi; Department of Computer Engineering (1994)
Non-uniform cellular automata networks having capability of classification and computation.
Kılıç, Hürevren; Department of Computer Engineering (1992)
Non-Abelian gauge theories of the Yang-Mills type
Abuhatab, Ahmed; Başkal, Sibel; Department of Physics (2003)
In this thesis, starting from the effective Lagrangians of the standard Yang-Mills, higher derivative Yang-Mills and the Chern-Simons- Yang-Mills theories, we have given the corresponding field equations and the symmetric gauge invariant energy- momentum tensors. Lagrangians containing higher derivative terms have been found useful for discussing the long lange effects of the gluon fields. A numeri cal solution is found for a spherically symmetric static gauge potential. On the other hand, Chern-Simons- Yan...
Non-normal bivariate distributions: estimation and hypothesis testing
Qunsiyeh, Sahar Botros; Tiku, Moti Lal; Department of Statistics (2007)
When using data for estimating the parameters in a bivariate distribution, the tradition is to assume that data comes from a bivariate normal distribution. If the distribution is not bivariate normal, which often is the case, the maximum likelihood (ML) estimators are intractable and the least square (LS) estimators are inefficient. Here, we consider two independent sets of bivariate data which come from non-normal populations. We consider two distinctive distributions: the marginal and the conditional dist...
NON-EUCLIDEAN VECTOR PRODUCT FOR NEURAL NETWORKS
Afrasiyabi, Arman; Badawi, Diaa; Nasır, Barış; Yildiz, Ozan; Yarman Vural, Fatoş Tunay; ÇETİN, AHMET ENİS (2018-04-20)
We present a non-Euclidean vector product for artificial neural networks. The vector product operator does not require any multiplications while providing correlation information between two vectors. Ordinary neurons require inner product of two vectors. We propose a class of neural networks with the universal approximation property over the space of Lebesgue integrable functions based on the proposed non-Euclidean vector product. In this new network, the "product" of two real numbers is defined as the sum ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
S. Bakırkaya, “Non-uniform cellular finite automata neural networks,” Middle East Technical University, 1994.