Genetic algorithm-aided dynamic fuzzy rule interpolation

Nitin Naik, Ren Diao, Qiang Shen

Research output: Chapter in Book/Published conference outputConference publication


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 languageEnglish
Title of host publicationProceedings of the 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE
Number of pages8
ISBN (Electronic)9781479920723
Publication statusPublished - 4 Sept 2014
Event2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584


Conference2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Copyright 2015 Elsevier B.V., All rights reserved.


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