This paper describes a method for rule base compression of fuzzy systems. The method compresses a fuzzy system with an arbitrarily large number of rules into a smaller fuzzy system by removing the redundancy in the fuzzy rule base. As a result of this compression, the number of on-line operations during the fuzzy inference process is significantly reduced without compromising the solution. This rule base compression method outperforms significantly other known methods for fuzzy rule base reduction.Peer Reviewe
International audienceThe problem of learning fuzzy rule-bases is analyzed from the perspective of f...
The characteristics of a fuzzy model are frequently determined by the manner in which the rules are ...
Several techniques have been proposed for making inferences using the information contained in an in...
This paper describes a method for rule base compression of fuzzy systems. The method compresses a fu...
Fuzzy rule base simplification is used to reduce the complexity of fuzzy models identified from the ...
Fuzzy rule base simplification is used to reduce the complexity of fuzzy models identified from the ...
Thesis (Ph. D.)--University of Washington, 2004Despite ever-growing processor speed, application of ...
This paper aims the introduction and comparison of two novel fuzzy system generation methods that im...
This paper focuses on two essential topics of the fuzzy area. The first is the reduction of fuzzy ru...
Due to its high performance and comprehensibility, fuzzy modelling is becoming more and more popular...
In this study, we develop a comprehensive design process of granular fuzzy rule-based systems. These...
International audienceDriven by the growing complexity of real-world systems, current trends in fuzz...
AbstractThe two most important models of inferencing in approximate reasoning with fuzzy sets are Za...
An objective of merging rules in rule bases designed for system modeling and function approximation ...
International audienceThe problem of learning fuzzy rule-bases is analyzed from the perspective of f...
The characteristics of a fuzzy model are frequently determined by the manner in which the rules are ...
Several techniques have been proposed for making inferences using the information contained in an in...
This paper describes a method for rule base compression of fuzzy systems. The method compresses a fu...
Fuzzy rule base simplification is used to reduce the complexity of fuzzy models identified from the ...
Fuzzy rule base simplification is used to reduce the complexity of fuzzy models identified from the ...
Thesis (Ph. D.)--University of Washington, 2004Despite ever-growing processor speed, application of ...
This paper aims the introduction and comparison of two novel fuzzy system generation methods that im...
This paper focuses on two essential topics of the fuzzy area. The first is the reduction of fuzzy ru...
Due to its high performance and comprehensibility, fuzzy modelling is becoming more and more popular...
In this study, we develop a comprehensive design process of granular fuzzy rule-based systems. These...
International audienceDriven by the growing complexity of real-world systems, current trends in fuzz...
AbstractThe two most important models of inferencing in approximate reasoning with fuzzy sets are Za...
An objective of merging rules in rule bases designed for system modeling and function approximation ...
International audienceThe problem of learning fuzzy rule-bases is analyzed from the perspective of f...
The characteristics of a fuzzy model are frequently determined by the manner in which the rules are ...
Several techniques have been proposed for making inferences using the information contained in an in...