Huang, Zhiheng

Shen, Qiang

20

2006-05-02T17:37:38Z

2006-05-02T17:37:38Z

2006

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.

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IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 14, NO. 2, APRIL 2006

DOI: 10.1109/TFUZZ.2005.859324

http://hdl.handle.net/1842/908

en

IEEE

Fuzzy model simplification

fuzzy rule interpolation

scale and move transformations

sparse rule base

transformation-based interpolation

Fuzzy Interpolative Reasoning via Scale and Move Transformations

Article