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Permanent link (DOI): https://doi.org/10.7939/R3M32NM81

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COMPUTATIONAL INTELLIGENCE-BASED TECHNIQUES IN THE CONSTRUCTION AND REDUCTION OF RULE-BASED SYSTEMS Open Access

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Other title
Subject/Keyword
Intelligent Systems
Software Engineering
Fuzzy neural network
Rule-based systems
Fuzzy c-means
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Li, Kuwen
Supervisor and department
Dr. Witold Pedrycz and Dr. Marek Reformat (Department of Electrical and Computer Engineering)
Examining committee member and department
Dr. Mihaela Ulieru (IMPACT Institute for the Digital Economy)
Dr. Petr Musilek (Department of Electrical and Computer Engineering)
Dr. Jozef Szymanski (Department of Civil and Environmental Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Software Engineering and Intelligent Systems
Date accepted
2013-09-30T14:07:30Z
Graduation date
2013-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
This dissertation focuses on applying Computational Intelligence, a consortium of the technologies of fuzzy sets, neurocomputing and evolutionary computing, to the design and analysis of fuzzy rule-based systems (FRBS). We discuss two methods to construct FRBS, where the crux of the method is to seamlessly be based on the fuzzy neural network (FNN) and fuzzy c-means (FCM) clustering respectively. The rule set for the FRBS derived from the above two methods commonly consists of quite a number of rules and including all the attributes from the problem inputs. It becomes necessary and intuitive to reduce the dimensionality (number of input attributes in the rule) of the rules in the rule set. Also, some rules in the rule set might be conflicting with others. To make the FRBS more concise, the less important rules could be removed from the rule set. So after finishing the construction of FRBS, the rule complexity reduction algorithms are applied. The key results of this study include: • Construction of the FRBS with the aid of FNNs where the network is developed through genetic optimization. • Reduction of complexity in terms of dimensionality and quantity (viz. the number of rules) by configuring pruning thresholds for AND neurons and OR neurons. The optimal values of the thresholds are determined in a way one strikes a sound balance between the interpretability of the rules and the accuracy associated with the reduced (simplified) rules. To develop the model optimal against these two competing objectives, multi-objective optimization is considered. • Application of FRBS constructed with the use of FNN to well-known datasets and a real-world application such as deployment of wireless sensor networks. • Construction of FRBS involving mechanism of information granulation (fuzzy clustering) and local linear models and studies on their complexity management through reduction of condition space and a relational expansion of fuzzy clusters.
Language
English
DOI
doi:10.7939/R3M32NM81
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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