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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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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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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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Detection of IoT-botnet attacks using fuzzy rule interpolation
Journal of Intelligent & Fuzzy Systems, 2020Recently, the Internet of Things (IoT) has been used in technology for different aspects to increase the efficiency and comfort of human life. Protecting the IoT infrastructure is not a straightforward task.
M. Alkasassbeh +3 more
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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.
Thomas A. Sudkamp, Robert J. Hammell II
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Extending the concept of Fuzzy Rule Interpolation with the interpolation of fuzziness
2012 IEEE International Conference on Fuzzy Systems, 2012Fuzzy Rule Interpolation (FRI) methods are not always suitable for describing changes in the conclusion fuzziness. For example, it is difficult to describe cases in which the conclusion for a crisp observation must be fuzzy, or in which an increase in the fuzziness of an observation yields less fuzziness in the conclusion.
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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 0008
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Dynamic Density-Based Fuzzy Rule Interpolation with Application to Mammography Abnormality Detection
IEEE International Conference on Fuzzy SystemsFuzzy rule interpolation (FRI) offers a powerful solution for deducing conclusions when observations fail to match existing rules in a sparse fuzzy rule base.
Jinle Lin +3 more
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On the Selection the Rule Membership Functions and Fuzzy Rule Interpolation
Studies in Computational Intelligence, 2021Szilvia Nagy +4 more
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A Feature-Driven Approach to Adaptive Fuzzy Rule Interpolation
IEEE International Conference on Fuzzy SystemsFuzzy Rule Interpolation (FRI) is an effective approach for reasoning under uncertainty in sparse rule-based systems. However, traditional FRI methods rely on a fixed number of rules, which perform poorly in high-dimensional complex datasets.
Xin Wang +6 more
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