Fuzzy Control System In Excel

Wed March 11, 2020
fuzzy type1-fuzzy excel

I am currently extending my Type-2 Fuzzy Logic Library so that fuzzy controllers can be implemented through its codebase. The implementation is straightforward if you have the correct structures in place; using an inference system, the most popular of which was discussed by Mamdani (Mamdani, Ebrahim H. “Application of fuzzy algorithms for control of simple dynamic plant.” Proceedings of the institution of electrical engineers. Vol. 121. No. 12. IET, 1974.). An illustration of the Mamdani inference system operation is below (taken from wikipedia):

Mamdani FIS

Testing such a system can be a challenge and for this reason I have created a version of Mamdani FIS in Excel. The structure is quite simple; the workbook implements a 2-input, single output system with 2 rules that can be easily extended if necessary. The inputs and outputs contain 3 fuzzy sets each. An ‘input and results’ sheet provides a mechanism so that the discrete inputs are entered for each variable and the resulting fuzzy output can be observed.

The sheet can be downloaded here

Paper Implementation - Uncertain rule-based fuzzy logic systems Introduction and new directions-Jerry M. Mendel; Prentice-Hall, PTR, Upper Saddle River, NJ, 2001,    555pp., ISBN 0-13-040969-3. Example 9-4, page 261

October 8, 2022
type2-fuzzy type2-fuzzy-library fuzzy python IT2FS paper-workout

Type Reduction of Interval Type-2 Fuzzy Sets

April 6, 2022
type2-fuzzy type2-fuzzy-library fuzzy python IT2FS

Paper Implementation - C. Wagner and H. Hagras. 'Toward general type-2 fuzzy logic systems based on zSlices.'

A look at C. Wagner and H. Hagras. 'Toward general type-2 fuzzy logic systems based on zSlices.', working of paper examples using T2Fuzz Library
type2-fuzzy paper-workout type2-fuzzy-library fuzzy python
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