Journal
FUZZY SETS AND SYSTEMS
Volume 361, Issue -, Pages 71-87Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.fss.2018.09.001
Keywords
Fuzzy rule-based model; Fuzzy clustering; Basis functions approximation; Randomization algorithms
Funding
- Canada Research Chairs(CRC) Program
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- China Scholarship Council [201306110018]
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Fuzzy rule-based models have attracted attention because of their modular architectures, well-developed design methodologies and practices as well as interpretability aspects. Methods exploiting factors of randomness offer significant efficiency and implementation simplicity that are essential in numerous application areas. In this study, we propose an original development of fuzzy rule-based models established with the aid of concepts of randomization algorithms. Several design strategies involving different random prototypes generation and basis functions approximation are studied. We investigate performance aspects of randomized rule-base and look at the performance versus the key components of the models such as the number of rules and the use of the randomized algorithms in the development. Furthermore, a comparative study is offered to quantify the efficiency of randomized algorithms. Experimental studies are reported for a series of publicly available data sets to illustrate the effectiveness of the proposed method and discuss its main features. (C) 2018 Elsevier B.V. All rights reserved.
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