Towards Fuzzy-Rough Rule Interpolation
نویسندگان
چکیده
Fuzzy rule interpolation is an important technique for performing inferences with sparse rule bases. Even when given observations have no overlap with the antecedent values of any rule, fuzzy rule interpolation may still derive a conclusion. Nevertheless, fuzzy rule interpolation can only handle fuzziness but not roughness. Rough set theory is a useful tool to deal with incomplete knowledge, which handles roughness but not fuzziness. Fuzzy rough sets are used to extend the original concepts in rough sets. This paper proposes a novel rule interpolation method which integrates fuzzy-rough representations with rule interpolation to deal with both fuzziness and roughness. The method follows the approach of [1], [2], using transformationbased techniques to perform interpolation, and can deal with rule interpolation in a more flexible and more robust way.
منابع مشابه
Rough-fuzzy rule interpolation
Fuzzy rule interpolation forms an important approach for performing inference with systems comprising sparse rule bases. Even when a given observation has no overlap with the antecedent values of any existing rules, fuzzy rule interpolation may still derive a useful conclusion. Unfortunately, very little of the existing work on fuzzy rule interpolation can conjunctively handle more than one for...
متن کاملTransformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets
In support of reasoning with sparse rule bases, fuzzy rule interpolation (FRI) offers a helpful inference mechanism for deriving an approximate conclusion when a given observation has no overlap with any rule in the existing rule base. One of the recent and popular FRI approaches is the scale and move transformation-based rule interpolation, known as T-FRI in the literature. It supports both in...
متن کاملTowards Fuzzy Interpolation with “at Least–at Most” Fuzzy Rule Bases
Fuzzy interpolation property is among the most important properties of fuzzy inference systems. It has been showed that the normality plus Ruspini condition applying to the antecedent fuzzy sets is a sufficient condition with a high practical impact. Another important property is the monotone behavior of the resulting control function (after a defuzzification) derived from a monotone fuzzy rule...
متن کاملA logical approach to interpolation based on similarity relations
One of the possible semantics of fuzzy sets is in terms of similarity, namely a grade of membership of an item in a fuzzy set can be viewed as the degree of resemblance between this item and prototypes of the fuzzy set. In such a framework, an interesting question is how to devise a logic of similarity, where inference rules can account for the proximity between interpretations. The aim is to c...
متن کاملTowards a Theory of a Fuzzy Rule Base Interpolation
It is well known that a fuzzy rule base is a characterization of a partially given mapping (fuzzy function) between fuzzy universes. For practical applications, it is desirable to interpolate that function in order to compute its values at points (fuzzy or crisp) other than fuzzy sets (nodes) in antecedents of the rule base. Moreover, interpolation requires that in the case of coincidence betwe...
متن کامل