Fuzzy rule systems have found a wide range of applications in many fields of science and
technology. Traditionally, fuzzy rules are generated from human expert knowledge or human …

Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be
designed either from expert knowledge or from data. For complex systems, FIS based on …

Sophisticated fuzzy rule systems are supposed to be flexible, complete, consistent and
compact (FC/sup 3/). Flexibility, and consistency are essential for fuzzy systems to exhibit an …

Fuzzy rule-based systems are one of the most important areas of application of fuzzy sets
and fuzzy logic. Constituting an extension of classical rule-based systems, these have been …

We propose a new method for fuzzy rule extraction from data by a genetic algorithm and a
fine tuning of the extracted membership functions by a constrained nonlinear optimization …

The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule
generation algorithms. Rule generation from artificial neural networks is gaining in …

The problem of learning fuzzy rule bases is analyzed from the perspective of finding a
favorable balance between the accuracy of the system, the speed required to learn the rules …

This paper presents a genetic algorithm (GA) that automatically constructs the knowledge
base used by fuzzy decision support systems (FDSS). The GA produces an optimal …

This paper shows how a small number of fuzzy rules can be selected for designing
interpretable fuzzy rule-based classification systems. Our approach consists of two phases …

This paper proposes a new approach to fuzzy rule generation from a set of examples with
fuzzy representation. The new approach called fuzzy extension matrix incorporates the fuzzy …