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Granular Fuzzy Rule-Based Models: Design and Analysis Open Access


Other title
coverage and specificity
allocation of information granularity
fuzzy rule-based model
granular computing
Type of item
Degree grantor
University of Alberta
Author or creator
Hu, Xingchen
Supervisor and department
Witold Pedrycz (Electrical and Computer Engineering)
Examining committee member and department
Alireza Sadeghian (Ryerson University)
Ergun Kuru (Civil and Environmental Engineering)
Mojgan Daneshmand (Electrical and Computer Engineering)
Marek Reformat (Electrical and Computer Engineering)
Department of Electrical and Computer Engineering
Software Engineering and Intelligent Systems
Date accepted
Graduation date
2017-11:Fall 2017
Doctor of Philosophy
Degree level
Fuzzy rule-based models have been studied for decades and emerged in a diversity of architectures and design approaches. They play a vital and unique role by forming a human-centric computing framework. In general, fuzzy rule-based models are regarded as numeric constructs; as such they are optimized and evaluated at the numeric level. However, no ideal fuzzy models that fully capture (coincide with) all numeric experimental data. Information granularity is a perspective to represent and recognize the abstraction of information as the observation method of humans. In this perspective, the numeric data can be presented at various levels of resolution or scales. By bringing a concept of information granularity into fuzzy rule-based models, we give up on obtaining precise numeric models, whereas we make them into granular form and produce granular results. Subsequently, the outputs provided by granular fuzzy rule-based models are aligned well with the experimental data and deliver better insight into credibility. The fundamental objective of this thesis is to establish a comprehensive, systematic method for developing granular fuzzy rule-based models, so that the granular outputs of the models can embrace (cover) the target experimental data as much as possible, meanwhile the granular outputs are as specific as possible. To accomplish these objectives, we study several fundamental design issues that emerge in the realm of Granular Computing. First, we propose an advanced scheme of granulation and degranulation to abstract information granules from numeric data. Second, we investigate several commonly known logic operators that are used in fuzzy modeling and granular fuzzy modeling. Afterwards, we design a series of development strategies for granular fuzzy rule-based models by admitting and allocating a certain level of information granularity around numeric values. Our proposed granular rule-based models could be classified into three groups: granular input space of the models, granular processing modules of the models, and granular output space of the models. Unlike the standard numeric-performance measure of fuzzy models that come in the form of the root-mean-square error (RMSE), two pertinent performance measures are introduced and implemented to evaluate the performance of granular fuzzy rule-based models: namely, coverage and specificity. We develop several protocols of forming and allocating information granules to cope with different strategies of granular modeling and analyze how different protocols lead to improve the performance of granular models. Some commonly used population-based optimization algorithms—for instance, particle-swarm optimization (PSO) and differential evolution (DE)—are used to optimize the allocation of information granularity, and coverage and specificity criteria are used to guide the optimization. A series of experimental studies is reported which offers a comprehensive overview of the underlying realization and performance of the granular fuzzy rule-based models.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
X Hu, W Pedrycz, G Wu, X Wang. "Data reconstruction with information granules: An augmented method of fuzzy clustering." Applied Soft Computing 55 (2017): 523-532.X Hu, W Pedrycz, X Wang. "Comparative analysis of logic operators: a perspective of statistical testing and granular computing." International Journal of Approximate Reasoning 66 (2015): 73-90.X Hu, W Pedrycz, X Wang. "Optimal allocation of information granularity in system modeling through the maximization of information specificity: A development of granular input space." Applied Soft Computing 42 (2016): 410-422.X Hu, W Pedrycz, O Castillo, P Melin. "Fuzzy rule-based models with interactive rules and their granular generalization." Fuzzy Sets and Systems 307 (2017): 1-28.X Hu, W Pedrycz, X Wang. "Granular Fuzzy Rule-based Models: A Study in a Comprehensive Evaluation of Fuzzy Models." IEEE Transactions on Fuzzy Systems (2016). DOI: 10.1109/TFUZZ.2016.2612300.X Hu, W Pedrycz, X Wang. "From fuzzy rule-based models to their granular generalizations." Knowledge-Based Systems 124 (2017): 133-143.X Hu, W Pedrycz, X Wang. "Development of Granular Models Through the Design a Granular Output Spaces." Knowledge-Based Systems 134 (2017): 159–171.

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