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Permanent link (DOI): https://doi.org/10.7939/R3J960J49

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Optimal Allocation of Information Granularity to the Inputs of Granular Neural Networks Open Access

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Other title
Subject/Keyword
Granular Computing
Optimal Granularity Allocation
Sensitivity Analysis
Granular Neural Networks
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Akhoundi, Elaheh
Supervisor and department
Pedrycz, Witold (Electrical and Computer Engineering)
Reformat, Marek (Electrical and Computer Engineering)
Examining committee member and department
Pedrycz, Witold (Electrical and Computer Engineering)
Musilek, Petr (Electrical and Computer Engineering)
Reformat, Marek (Electrical and Computer Engineering)
Szymanski, Jozef (Civil and Environmental Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Software Engineering and Intelligent Systems
Date accepted
2014-09-24T15:12:03Z
Graduation date
2014-11
Degree
Master of Science
Degree level
Master's
Abstract
In this thesis, we propose a design process to construct granular neural networks with granular inputs and numeric network parameters. The proposed granular network is formed on the basis of a numeric neural network whose inputs are augmented using probabilistic information granules. The design problem is formulated as an optimization problem which aims to allocate a given level of information granularity to the inputs of the network such that the specificity of the network outputs gets maximized. The resulting optimization problem is solved analytically and the derived solution determines the optimal granularity levels corresponding to the input features of the granular neural network. The proposed design process is then used to construct granular neural networks for several synthetic and real data sets.
Language
English
DOI
doi:10.7939/R3J960J49
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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