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Comparative Analysis of Fuzzy Rule Interpolation Techniques Across Various Scenarios Using a Set of Benchmarks [PDF]
IEEE AccessThis paper presents a set of benchmarks to evaluate the performance of Fuzzy Rule Interpolation (FRI) methods under various challenging conditions. FRI methods are widely used for handling sparse fuzzy rule bases and reducing decision complexity. Despite
Maen Alzubi +4 more
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Fuzzy Rule Interpolation Methods and Fri Toolbox [PDF]
CoRR, 2019 FRI methods are less popular in the practical application domain. One possible reason is the missing common framework. There are many FRI methods developed independently, having different interpolation concepts and features. One trial for setting up a common FRI framework was the MATLAB FRI Toolbox, developed by Johanyák et. al. in 2006.
Maen Alzubi +2 more
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Fuzzy Rule Interpolation by the Conservation of Relative Fuzziness
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2000 If the number of variables is growing the size of fuzzy rule bases increase exponentially. To reduce size and inference/control time, it is often necessary to deal with sparse rule bases. In such bases, classic algorithms such as the CRI of Zadeh and the Mamdani-method do not function.
László T. Kóczy +2 more
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Transformation-Based Fuzzy Rule Interpolation With Mahalanobis Distance Measures Supported by Choquet Integral [PDF]
IEEE transactions on fuzzy systems, 2022 Fuzzy rule interpolation (FRI) strongly supports approximate inference when a new observation matches no rules, through selecting and subsequently interpolating appropriate rules close to the observation from the given (sparse) rule base.
Mou Zhou +7 more
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Dynamic Fuzzy Rule Interpolation
, 2015 Designers of effective and efficient fuzzy systems have long recognised the value of inferential hybridity in the implementation of sparse fuzzy rule based systems. Which is to say: such systems should have recourse to fuzzy rule interpolation (FRI) only when no rule matches a given observation; otherwise, when an observation partially or exactly ...
Nitin P. Kumar
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Towards utilization of rule base structure to support fuzzy rule interpolation [PDF]
Expert Syst. J. Knowl. Eng., 2022 Fuzzy rule interpolation (FRI) offers a reliable approach for providing an interpretable approximate decision with a sparse rule base, when a new observation does not match any existing rules.
Changhong Jiang +3 more
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Monotone Fuzzy Rule Interpolation for TSK-FIS-Like n-Ary Aggregation Functions [PDF]
IEEE International Conference on Fuzzy Systems, 2023 Fuzzy Rule Interpolation (FRI) is important for fuzzy inference systems modeling pertaining to a sparse fuzzy rule base system. The focus of this paper is on a specific class of FRI, i.e., monotone FRI (MFRI), for modeling monotone Takagi-Sugeno-Kang ...
Chian Haur Jong +3 more
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Higher Order Fuzzy Rule Interpolation
, 2015 Fuzzy inference is an effective means for representing and handling vagueness and imprecision. As a particular type of fuzzy inference, fuzzy rule interpolation enhances the performance of the inference when a given observation has no overlap with the antecedent values of any of the existing rules.
Chengyuan
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Curvature-based sparse rule base generation for fuzzy rule interpolation [PDF]
Journal of Intelligent & Fuzzy Systems, 2019 Fuzzy inference systems have been successfully applied to many real-world applications. Traditional fuzzy inference systems are only applicable to problems with dense rule bases covering the entire problem domains, whilst fuzzy rule interpolation (FRI) works with sparse rule bases that do not cover certain inputs.
Yao Tan +4 more
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Similarity, interpolation, and fuzzy rule construction
Fuzzy Sets and Systems, 1993 Abstract A method for the construction of fuzzy concepts and fuzzy if-then rules based on similarity and paradigmatic examples is presented. It is shown that all normal fuzzy sets may be realized as the interpolation of paradigmatic examples by a similarity relation.
Thomas Sudkamp
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