39 research outputs found
Fuzzy interpolative reasoning via scale and move transformation
Interpolative reasoning does not only help reduce the
complexity of fuzzy models but also makes inference in sparse
rule-based systems possible. This paper presents an interpolative
reasoning method by means of scale and move transformations. It
can be used to interpolate fuzzy rules involving complex polygon,
Gaussian or other bell-shaped fuzzy membership functions. The
method works by first constructing a new inference rule via
manipulating two given adjacent rules, and then by using scale
and move transformations to convert the intermediate inference
results into the final derived conclusions. This method has three
advantages thanks to the proposed transformations: 1) it can
handle interpolation of multiple antecedent variables with simple
computation; 2) it guarantees the uniqueness as well as normality
and convexity of the resulting interpolated fuzzy sets; and 3) it suggests
a variety of definitions for representative values, providing
a degree of freedom to meet different requirements. Comparative
experimental studies are provided to demonstrate the potential of
this method
Fuzzy Interpolation Systems and Applications
Fuzzy inference systems provide a simple yet effective solution to complex non-linear problems, which have been applied to numerous real-world applications with great success. However, conventional fuzzy inference systems may suffer from either too sparse, too complex or imbalanced rule bases, given that the data may be unevenly distributed in the problem space regardless of its volume. Fuzzy interpolation addresses this. It enables fuzzy inferences with sparse rule bases when the sparse rule base does not cover a given input, and it simplifies very dense rule bases by approximating certain rules with their neighbouring ones. This chapter systematically reviews different types of fuzzy interpolation approaches and their variations, in terms of both the interpolation mechanism (inference engine) and sparse rule base generation. Representative applications of fuzzy interpolation in the field of control are also revisited in this chapter, which not only validate fuzzy interpolation approaches but also demonstrate its efficacy and potential for wider applications
Some Considerations and a Benchmark Related to the CNF Property of the Koczy-Hirota Fuzzy Rule Interpolation
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 been suggested for unifying the common requirements an FRI methods have to satisfy. One of the most common one is the demand for a convex and normal fuzzy (CNF) conclusion, if all the rule antecedents and consequents are CNF sets. The KH FRI is the one, which cannot fulfill this condition. This paper is focusing on the conditions, where the KH FRI fails the demand for the CNF conclusion. By setting up some CNF rule examples, the paper also defines a Benchmark, in which other FRI methods can be tested if they can produce CNF conclusion where the KH FRI fails