Computing Derivatives in Interval Type-2 Fuzzy Logic Systems

IEEE Transactions on Fuzzy Systems

Volumn 12, Issue 1, 2004, Pages 84-98

  Mendel, Jerry M ^a  

^a University of Southern California ^*  (United States)

Author keywords

Derivations; Fuzzy logic system; Type 2 fuzzy logic system

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; FLOWCHARTING; FUNCTIONS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBLEM SOLVING;

DERIVATIONS; FUZZY LOGIC SYSTEMS (FLS); TYPE-2 FUZZY LOGIC SYSTEMS;

FUZZY SETS;

EID: 1542273540     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2003.822681     Document Type: Article

References (8)

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3. V. Kreinovich, A. Lakeyev, J. Rohn, and P. Kahl, Computational Complexity and Feasibility of Data Processing and Interval Computations. Dordrecht, The Netherlands: Kluwer, 1997, ch. 10.
4. Q. Liang and J. M. Mendel, "Interval type-2 fuzzy logic systems," presented at the FUZZ-IEEE '00, San Antonio, TX, May 2000.
5. _, "Interval type-2 fuzzy logic systems: theory and design," IEEE Trans. Fuzzy Syst., vol. 8, pp. 535-550, Aug. 2000.
6. J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Upper Saddle River, NJ: Prentice-Hall, 2001.
7. _, "On the importance of interval sets in type-2 fuzzy logic systems," presented at the Joint 9th IFSA World Congr. 20th NAFIPS In. Conf., Vancouver, BC, Canada, July 2001.
8. L.-X. Wang and J. M. Mendel, "Fuzzy basis functions, universal approximation, and orthogonal least-squares learning," IEEE Trans. Neural Networks, vol. 3, pp. 807-813, Sept. 1992.