Abstract
Fuzzy rule interpolation (FRI) is a well established area for reducing the complexity of fuzzy models and for making inference possible in sparse rule-based systems. Regardless of the actual FRI approach employed, the interpolative reasoning process generally produces a large number of interpolated rules, which are then discarded as soon as the required outcomes have been obtained. However, these interpolated rules may contain potentially useful information, e.g., covering regions that were uncovered by the original sparse rule base. Thus, such rules should be exploited in order to develop a dynamic rule base for improving the overall system coverage and efficacy. This paper presents a genetic algorithm based dynamic fuzzy rule interpolation framework, for the purpose of selecting, combining, and promoting informative, frequently used intermediate rules into the existing rule base. Simulations are employed to demonstrate the proposed method, showing better accuracy and robustness than that achievable through conventional FRI that uses just the original sparse rule base.
Original language | English |
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Title of host publication | Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE |
Publisher | IEEE |
Pages | 2198-2205 |
Number of pages | 8 |
ISBN (Electronic) | 9781479920723 |
DOIs | |
Publication status | Published - 4 Sept 2014 |
Event | 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014 |
Publication series
Name | IEEE International Conference on Fuzzy Systems |
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ISSN (Print) | 1098-7584 |
Conference
Conference | 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 |
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Country/Territory | China |
City | Beijing |
Period | 6/07/14 → 11/07/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.