Some Considerations and a Benchmark Related to the CNF Property of the Koczy-Hirota Fuzzy Rule Interpolation
Maen Alzubi, Szilveszter Kovacs

TL;DR
This paper examines the limitations of the Koczy-Hirota Fuzzy Rule Interpolation method regarding the CNF property, highlights problematic cases, and provides a benchmark for testing other FRI methods' ability to produce CNF conclusions.
Contribution
It identifies specific conditions where KH FRI fails to produce CNF conclusions and introduces a benchmark set for evaluating other FRI methods against these issues.
Findings
KH FRI cannot always produce CNF conclusions in certain cases.
A benchmark set of examples is proposed for testing FRI methods.
The paper highlights the need for FRI methods to satisfy CNF conditions.
Abstract
The goal of this paper is twofold. Once to highlight some basic problematic properties of the KH Fuzzy Rule Interpolation through examples, secondly to set up a brief Benchmark set of Examples, which is suitable for testing other Fuzzy Rule Interpolation (FRI) methods against these ill conditions. Fuzzy Rule Interpolation methods were originally proposed to handle the situation of missing fuzzy rules (sparse rule-bases) and to reduce the decision complexity. Fuzzy Rule Interpolation is an important technique for implementing inference with sparse fuzzy rule-bases. Even if a given observation has no overlap with the antecedent of any rule from the rule-base, FRI may still conclude a conclusion. The first FRI method was the Koczy and Hirota proposed "Linear Interpolation", which was later renamed to "KH Fuzzy Interpolation" by the followers. There are several conditions and criteria have…
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