Learning flexible structured linguistic fuzzy rules for Mamdani fuzzy systems

Authors:
Ning Xiong
Affiliations:
School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden
Venue:
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Year:
2009

Citing 4
Cited 0

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Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction

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Cooperative Evolutionary Learning of Linguistic Fuzzy Rules and Parametric Aggregation Connectors for Mamdani Fuzzy Systems

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Simultaneous design of membership functions and rule sets for fuzzycontrollers using genetic algorithms

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Abstract

One significant challenge in building fuzzy systems for complex problems is the "curse of dimensionality". For the sake of a reduced size of the knowledge base, some rules with incomplete premise structures covering larger areas of the input domain are often desirable. This paper presents a genetic algorithm based approach to searching for suitable antecedents of rules under which specific fuzzy consequences can be recommended. The rule premises are coded in a flexible way allowing for presence as well as absence of an input variable in them, in combination with a certain class of input and output fuzzy sets. On the other hand, a consistency index is introduced to give a numerical evaluation of the coherence among individual rules. This index is incorporated into the fitness function of the genetic algorithm to search for a set of optimal rule premises yielding not only good problem solving performances but also little conflict in the rule base. The effectiveness of our work is demonstrated through experiment results on controlling an inverted pendulum.