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Fuzzy Rule Interpolation Based on Polar Cuts (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Johanyák, Zsolt Csaba Kovács, Szilveszter |
| Description | This content is published in/by Computational Intelligence, Theory and Applications, Springer Berlin Heidelberg |
| Abstract | Systems applying fuzzy logic are rule based ones. The collection of the rules the so called rule base can be characterized as dense or sparse depending on whether there exist rules for all the possible observations. In the sparse case for some observations there are no rules whose antecedent part would overlap the observation at least partially. Therefore the classical compositional reasoning methods can not produce an acceptable conclusion. The inference techniques based on fuzzy rule interpolation are developed for especially this purpose. This paper proposes a new fuzzy rule interpolation based inference technique applying the concept of linguistic term shifting and polar cut. It is called FRIPOC (Fuzzy Rule Interpolation based in POlar Cuts) and it is applicable in the case of sparse and dense rule bases, too. Its main advantages are its comprehensibility, extrapolation capability and its applicability even if the height of one or more fuzzy sets is smaller than one. The rest of this paper is organized as follows. Section 1 gives a brief overview on the relevant fuzzy rule interpolation techniques grouping them |
| File Format | |
| Publisher Date | 2006-01-01 |
| Access Restriction | Open |
| Subject Keyword | Polar Cut Fuzzy Rule Interpolation Inference Technique Acceptable Conclusion Classical Compositional Main Advantage Fuzzy Set Linguistic Term Relevant Fuzzy Rule Interpolation Technique Dense Rule Base Possible Observation Extrapolation Capability Antecedent Part Brief Overview New Fuzzy Rule Interpolation Fuzzy Logic Called Rule Base Sparse Case |
| Content Type | Text |
| Resource Type | Article |
