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 ...
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DOI
10.12681/eadd/38490
Handle URL
http://hdl.handle.net/10442/hedi/38490
ND
38490
Alternative title
Ανάπτυξη και θεμελίωση μεθόδων για αξιόπιστους νευρωνικούς υπολογισμούς
Author
Adam, Stavros (Father's name: Panagiotis)
Date
2016
Degree Grantor
University of Patras
Committee members
Βραχάτης Μιχαήλ
Μαγουλάς Γεώργιος
Καρράς Δημήτριος
Discipline
Natural SciencesMathematics
Natural SciencesComputer and Information Sciences
Keywords
Reliable algorithms and computations; Interval analysis; Swarm intelligence; Neural networks; Clustering; Kohonen networks; Multilayer perceptrons; Complexity regularization; Zero norm; Global optimization; NEURAL NETWORK TRAINING; Neural network inversion; Statistical data analysis
Country
Greece
Language
Greek
Description
250 σ., tbls., fig., ch., ind.
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