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Permanent link (DOI): https://doi.org/10.7939/R32M6Z

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Learning and aggregation of Fuzzy Cognitive Maps - an evolutionary approach Open Access

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Other title
Subject/Keyword
Fuzzy Cognitive Maps, real-coded genetic algorithms
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Stach, Wojciech J
Supervisor and department
Pedrycz, Witold (Electrical and Computer Engineering)
Kurgan, Lukasz (Electrical and Computer Engineering)
Examining committee member and department
Reformat, Marek (Electrical and Computer Engineering)
Dick, Scott (Electrical and Computer Engineering)
Achari, Gopal (Civil Engineering, University of Calgary)
Szymanski, Jozef (School of Mining & Petroleum Engineering)
Department
Department of Electrical and Computer Engineering
Specialization

Date accepted
2010-08-30T19:17:53Z
Graduation date
2010-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Fuzzy Cognitive Maps (FCMs) are a widely used, neuro-fuzzy based qualitative approach for the modeling of dynamic systems, which allow for both static and dynamic analyses. They are capable of modeling complex systems with nonlinearities and unknown physical behaviour. FCMs describe a given system by means of concepts connected by quantified cause-effect relationships. This dissertation contributes to the subject of computer-driven generation of FCMs that can be used to perform an accurate dynamic analysis of the modeled system. The dynamic analysis provides insights into the degree of presence, and dependencies between the concepts in successive iterations of the simulation of a given FCM model. Such simulation studies could be used to analyze “what-if” scenarios in the context of decision support and to perform time series predictions. Two research directions within the framework of FCM development, which concern the learning of FCMs from historical data and an aggregation of FCMs that were proposed by multiple experts, are investigated. Several new automated computational methods for data-driven learning and aggregation of FCMs are introduced and empirically evaluated. These methods utilize real-coded genetic algorithms (RCGA)-based optimization. This choice of the optimization vehicle was motivated by their well-documented efficiency in searching large and continuous search spaces, which are inherent to our problem. Experimental evaluation demonstrates that the proposed RCGA-based learning method outperforms modern existing approaches when the dynamic analysis is considered. A novel divide and conquer-based learning strategy to improve scalability of the RCGA approach, is also proposed. This strategy is shown to be competitive or even better than solutions based on the parallelization of the underlying genetic algorithm. The RCGA-based learning method is further extended to provide improved FCMs when the number of connections of the map is known a priori. Experimental evaluation shows that the density-based learning method outperforms the generic RCGA-based approach when using a relatively accurate density estimate, and that both methods are equivalent when the estimate is inaccurate. In addition, a novel method for the aggregation of multiple input FCMs, is proposed. When compared to existing aggregation approaches, this method provides solutions that are more accurate when dynamic analysis is the objective.
Language
English
DOI
doi:10.7939/R32M6Z
Rights
License granted by Wojciech Stach (wstach@ualberta.ca) on 2010-08-27T19:36:01Z (GMT): 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 the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein 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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