Fuzzy Rule Interpolation Methods and Fri Toolbox
This work addresses the practical application gap for FRI methods by providing a standardized toolbox and comparative analysis, which is incremental as it builds on an existing framework.
The paper tackles the lack of a common framework for Fuzzy Rule Interpolation (FRI) methods by refreshing and extending the MATLAB FRI Toolbox, and it evaluates and compares ten FRI techniques using unified numerical benchmarks to classify them based on features like abnormality and linearity conditions.
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. The goals of this paper are divided as follows: firstly, to present a brief introduction of the FRI methods. Secondly, to introduce a brief description of the refreshed and extended version of the original FRI Toolbox. And thirdly, to use different unified numerical benchmark examples to evaluate and analyze the Fuzzy Rule Interpolation Techniques (FRI) (KH, KH Stabilized, MACI, IMUL, CRF, VKK, GM, FRIPOC, LESFRI, and SCALEMOVE), that will be classified and compared based on different features by following the abnormality and linearity conditions [15].