Fuzzy Rule Base Reduction

Dan Simon
Department of Electrical Engineering
Cleveland State University
1960 East 24th Street
Cleveland, OH 44115
 

Various attempts have been made over the years to reduce the rule base of a fuzzy logic system. Rule base reduction may be important for computational reasons in those cases where a fuzzy system has to be implemented in real time. Professor Yeung Yam and his colleagues have recently published an algorithm [2] based on singular value decomposition whereby a fuzzy rule base can be reduced. Professor Simon's submitted paper [1] has demonstrated the technique on a fuzzy estimator for motor winding current estimation, where the rule base was reduced from 49 rules to 9 rules. This site makes general-purpose MATLAB code available for fuzzy rule base reduction using Yam's algorithm. The code consists of two files that are zipped up in the file Reduce.zip.

The two files in Reduce.zip are Reduce.m (the main file) and FuzzFunc.m (an auxiliary file). Both files are necessary for the rule base reduction algorithm. In order to run the rule base reduction algorithm, perform the following steps.

  • Download the zip file by clicking on the above link.
  • Unzip Reduce.zip to get Reduce.m and FuzzFunc.m. If you don't have software to unzip the file, you can download a free evaluation version of WinZip from www.winzip.com.
  • Run MATLAB and make sure that the location of Reduce.m and FuzzFunc.m on your hard drive is part of your MATLAB path. For example, if you downloaded the files to the c:\reduce directory on your hard drive, type
    >> "path(path, 'c:\reduce');"
    at MATLAB's command prompt.
  • Type "Reduce" at the MATLAB prompt.

References

  1. D. Simon, “Design and Rule Base Reduction of a Fuzzy Filter for the Estimation of Motor Currents,” International Journal of Approximate Reasoning, vol. 25, pp. 145-167, October 2000 - pdf, 371 KB - postscript, 1.23 MB
  2. Y. Yam, P. Baranyi, and C. Yang, "Reduction of Fuzzy Rule Base Via Singular Value Decomposition," IEEE Transactions on Fuzzy Systems, Volume 7, Number 2, pp. 120-132, 1999.
  3. Y. Yam, "Fuzzy Approximation Via Grid Point Sampling and Singular Value Decomposition," IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, Volume 27, Number 6, pp. 933-951, 1997.

Professor Simon's Home Page

Department of Electrical and Computer Engineering

Cleveland State University

Last Revised: March 15, 2002