Development and foundation of methods for reliable neural computation
Abstract
This dissertation deals with the development of methods and algorithms offering reliable neural computation. An important part of the research has focused on providing the necessary theoretical basis for these developments. More specifically, research in this thesis focuses on handling uncertainty issues inherent in the data processing, that is, the algorithms used in neural computing. The questions that highlight and qualify the uncertainty in the data processing level are related to matters such as, the initial and boundary values of the algorithms, the robustness of the algorithms against computation or data errors, the multitude of heuristics which substitute mathematical modeling of unknown parameters etc. For modeling and resolving issues such as the above, we, mainly, adopted concepts and methods of Interval Analysis. We also used mathematical approaches such as global optimization and the zero norm of a vector. For specific analysis purposes we used clustering and data mining t ...
show more
![]() | |
![]() | Download full text in PDF format (7.4 MB)
(Available only to registered users)
|
All items in National Archive of Phd theses are protected by copyright.
|
Usage statistics

VIEWS
Concern the unique Ph.D. Thesis' views for the period 07/2018 - 07/2023.
Source: Google Analytics.
Source: Google Analytics.

ONLINE READER
Concern the online reader's opening for the period 07/2018 - 07/2023.
Source: Google Analytics.
Source: Google Analytics.

DOWNLOADS
Concern all downloads of this Ph.D. Thesis' digital file.
Source: National Archive of Ph.D. Theses.
Source: National Archive of Ph.D. Theses.

USERS
Concern all registered users of National Archive of Ph.D. Theses who have interacted with this Ph.D. Thesis. Mostly, it concerns downloads.
Source: National Archive of Ph.D. Theses.
Source: National Archive of Ph.D. Theses.