Towards More Efficient Type-2 Fuzzy Logic Systems

This work introduces representations and methods that improve the inferencing speeds of type-2 fuzzy logic systems by exploiting methods from computational geometry. A new representation of a secondary membership function is given along with its operations. We give the results from some initial experiments looking at the inferencing speed of a small, limited system. The results show a four and a half fold increase in the inferencing speed of our methods over a comparable standard system. Our motivation is that by improving the performance of type-2 systems such technologies may become more widely applied

[1]  Jerry M. Mendel,et al.  Interval type-2 fuzzy logic systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[2]  Jerry M. Mendel,et al.  Operations on type-2 fuzzy sets , 2001, Fuzzy Sets Syst..

[3]  J. Michael Spivey,et al.  The Z notation - a reference manual , 1992, Prentice Hall International Series in Computer Science.

[4]  Hani Hagras,et al.  A type-2 fuzzy embedded agent for ubiquitous computing environments , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[5]  I. Turksen Type 2 representation and reasoning for CWW , 2002 .

[6]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[7]  Hani Hagras,et al.  A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots , 2004, IEEE Transactions on Fuzzy Systems.

[8]  Robert Ivor John,et al.  A new and efficient method for the type-2 meet operation , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[9]  Thomas Ottmann,et al.  Algorithms for Reporting and Counting Geometric Intersections , 1979, IEEE Transactions on Computers.

[10]  Jerry M. Mendel,et al.  Type-2 fuzzy logic systems , 1999, IEEE Trans. Fuzzy Syst..

[11]  Jerry M. Mendel,et al.  Interval type-2 fuzzy logic systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[12]  Dongrui Wu,et al.  A type-2 fuzzy logic controller for the liquid-level process , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[13]  Jonathan M. Garibaldi,et al.  Effect of type-2 fuzzy membership function shape on modelling variation in human decision making , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[14]  Simon Coupland,et al.  Fuzzy logic and computational geometry , 2004 .

[15]  Peter R. Atherton,et al.  Hidden surface removal using polygon area sorting , 1977, SIGGRAPH.

[16]  R. John Type 2 fuzzy sets for knowledge representation and inferencing , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).