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Fuzzy Rule Interpolation with a Transformed Rule Base
2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021Traditional fuzzy rule interpolation (FRI) methods typically utilise Euclidean distances between an observation and the rules in a given sparse rule base to select a set of rules closest to the observation to perform interpolation. However, simply applying the Euclidean distance metric may frequently lead to inferior results, because it cannot take ...
Mou Zhou +5 more
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Backward rough-fuzzy rule interpolation
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015Fuzzy rule interpolation is an important technique for performing inference with 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 conclusion.
Chengyuan Chen +3 more
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Towards dynamic fuzzy rule interpolation
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013Fuzzy rule interpolation (FRI) offers a useful means for reducing the complexity of fuzzy models and more importantly, it makes inference possible in sparse rule-based systems. An interpolative reasoning system may encounter a large number of interpolated rules during the process of performing FRI, which are commonly discarded once the outcomes of the ...
Nitin Naik +3 more
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Towards hierarchical fuzzy rule interpolation
2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), 2015Fuzzy rule interpolation offers a useful means for enhancing the robustness of fuzzy models by making inference possible in systems of only a sparse rule base. However in practical applications, as the application domain of fuzzy systems expand to more complex ones, the “curse of dimensionality” problem of the conventional fuzzy systems became apparent,
Shangzhu Jin, Jun Peng
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Linear fuzzy rule base interpolation using fuzzy geometry
International Journal of Approximate Reasoning, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Das, Suman +2 more
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Background: Fuzzy Rule Interpolation
2018Conventional fuzzy reasoning methods such as Mamdani (Int J Man Mach Stud:7, 1975, [1]) and TSK (Fuzzy Sets Syst 28:15–33, 1988, [2], IEEE Trans Syst Man Cybern 1:116–132, 1985, [3]) require that the rule bases are dense. That is, the input universe of discourse is covered completely by the rule base.
Shangzhu Jin, Qiang Shen, Jun Peng
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Interpolation, completion, and learning fuzzy rules
IEEE Transactions on Systems, Man, and Cybernetics, 1994Fuzzy inference systems and neural networks both provide mathematical systems for approximating continuous real-valued functions. Historically, fuzzy rule bases have been constructed by knowledge acquisition from experts while the weights on neural nets have been learned from data.
T. Sudkamp, R.J. Hammell
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Interpolation in hierarchical fuzzy rule bases
Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), 2002A major issue in the field of fuzzy applications is the complexity of the algorithms used. In order to obtain efficient methods, it is necessary to reduce complexity without losing the easy interpretability of the components. One of the possibilities to achieve complexity reduction is to combine fuzzy rule interpolation with the use of hierarchical ...
Laszlo t. Koczy +2 more
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Feature ranking-guided fuzzy rule interpolation
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017Fuzzy rule interpolation (FRI) provides an alternative means to make inference with a sparse rule base, rather than directly resulting in failed reasoning when no rules can be fired for an input observation. However, existing approaches to FRI typically assume that rule antecedents are of equal significance in the implementation of interpolation ...
Fangyi Li +3 more
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Singular value-based fuzzy rule interpolation
Proceedings of IEEE International Conference on Intelligent Engineering Systems, 2002In sparse fuzzy rule bases, conventional fuzzy reasoning methods cannot reach a proper conclusion. To eliminate this problem interpolative reasoning has emerged in fuzzy research as a new topic. If the number of variables or the number of fuzzy terms is growing the size of the rule base increases exponentially, hence, the inference/control time also ...
P. Baranyi, null Yeung Yam, L.T. Koczy
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