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System modeling with granular architectures of computational intelligence Open Access


Other title
computational intelligence
system modeling
granular architectures
Type of item
Degree grantor
University of Alberta
Author or creator
Song, Mingli
Supervisor and department
Pedrycz, Witold (Electrical and Computer Engineering)
Examining committee member and department
Musilek, Petr (Electrical and Computer Engineering)
Reformat, Marek (Electrical and Computer Engineering)
Yao, Jingtao (Computer Science)
Pedrycz, Witold (Electrical and Computer Engineering)
Hicks, Faye (Civil and Environmental Engineering)
Department of Electrical and Computer Engineering

Date accepted
Graduation date
Doctor of Philosophy
Degree level
Information granules regarded as the key components of knowledge representation help generalize information while the level of granularity of information granules becomes crucial to the problem description and an overall strategy of problem solving in system modeling. The ultimate challenge is to develop a comprehensive model within which information granules serving as an important design asset helps realize problem solving. In this study, we propose a system modeling framework of granular architectures and evaluate its effectiveness. We develop a new approach to build functional rule-based fuzzy models by focusing on the reduction of input space which is realized by genetic algorithms. A concept and practice of a granular fuzzy model is established with interval results of global character by aggregating some collective sources of knowledge (local models). The granular fuzzy model based on interval analysis directly reflects upon the diversity of the local sources of knowledge in which intervals are constructed through the use of the principle of justifiable granularity. Two approaches are proposed to design granular neural networks. The first method is concerned with a formation of a global granular neural network whose architecture is formed as a result of reconciliation of outcomes produced by local neural networks. The second method is aimed at the realization of granular neural networks through the formation of interval (granular) connections around numeric connections of the original neural networks where single- and multiple-objective particle swarm optimization is used. We develop a granular analytic hierarchy process (AHP), which provides decision--makers a significant level of flexibility (expressed by the granular nature of the underling construct) so that their initial preferences can be adjusted within a certain interval to achieve higher level of consensus within the group. Moreover, a granulation of linguistic information used in the AHP model is adopted to elevate the consistency of the obtained solution. Throughout the overall study, particle swarm optimization is used as a comprehensive optimization framework to realize the design of granular constructs.
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