Abstract
In the area of fuzzy control, there are four inference methods, including Mamdani inference [17]. Larsen inference [13], Tsukamoto inference [19], and Takagi-Sugeno inference [18]. Common features of these inference methods are [4]: (1) the knowledge base is composed of fuzzy rules; (2) the uncertainty of reasoning is from the linguistic description of premises (or, sometimes consequences) or fuzzy rules (e.g., positively big, very bad, etc.); and (3) each rule in the knowledge base is believed to be completely creditable.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Bibliography
A. Basu, A. K. Majumdar, and S. Sinha, “An expert system approach to control system design and analysis,” IEEE Transactions on Systems, Man and Cybernetics, vol. 18, no. 5, pp. 685–694, Sept.–Oct. 1988.
C. E. Bozdag, C. Kahraman and D. Ruan, “Fuzzy group decision making for selection among computer integrated manufacturing systems,” Computers in Industry, vol. 51, no. 1, pp. 13–29, May 2003.
B. G. Buchanan and E. H. Shortliffe, Eds., Rule-Based Expert System: The MYCIN Experiments of the Stanford Heuristic Programming Project, Reading, MA: Addison-Wesley, 1984.
S. M. Chen, “A new approach to handling fuzzy decision-making problems,” IEEE Transactions on Systems, Man and Cybernetics, vol. 18, no. 6, pp. 1012–1016, Nov.–Dec. 1988.
F. Chiclana, F. Herrera, and E. Herrera-Viedma, “Integrating multiplicative preference relations in a multipurpose decision-making model based on fuzzy preference relations,” Fuzzy Sets and Systems, vol. 122, no. 2 pp. 277–291, Sept. 2001.
T. Fujita and S. Iwamoto, “An optimistic decision-making in fuzzy environment,” Applied Mathematics and Computation, vol. 120, no.1–3, pp. 123–137, May 2001.
M. B. Gorzalczany, “A method of inference in approximate reasoning based on interval-valued fuzzy sets,” Fuzzy Sets and Systems, vol. 21, no. 1, pp. 1–17, Jan. 1987.
S. J. Henkind and M. C. Harrison, “An analysis of four uncertainty calculi,” IEEE Transactions on Systems, Man and Cybernetics, vol. 18, no. 5, pp. 700–714, Sept.–Oct. 1988.
D. H. Hong and C. H. Choi, “Multicriteria fuzzy decision-making problems based on vague set theory,” Fuzzy Sets and Systems, vol. 114, no. 1, pp. 103–113, Aug. 2000.
K. Huan, Introduction to Expert Systems, Nanjing, China: Southeast University Press, 1988.
H. J. Huang and F. S. Wang, “Fuzzy decision-making design of chemical plant using mixed-integer hybrid differential evolution,” Computers and Chemical Engineering, vol. 26, no. 12, pp. 1649–1660, Dec. 2002.
K. J. Hunt, D. Sbarbaro, R. Bikowski, and P. J. Gawthrop, “Neural networks for control systems-A survey,” Automatica, vol. 28, no. 6, pp. 1083–1112, Nov. 1992.
P. M. Larsen, “Industrial applications of fuzzy logic control,” International Journal of Man-Machine Studies, vol. 12, no. 1, pp. 3–10, 1980.
N. S. Lee, Y. L. Grize, and K. Dehnad, “Quantitative models for reasoning under uncertainty in knowledge-based expert system,” International Journal of Intelligent Systems, vol. 2, no. 1, pp. 15–38, 1987.
D. Li, “Fuzzy multiattribute decision-making models and methods with incomplete preference information,” Fuzzy Sets and Systems, vol. 106, no. 2, pp. 113–119, Sept. 1999.
J. Lu, H. Zhang, and L. Chen, “A fuzzy control approach concerning with rule’s confidence,” Control and Decision, vol. 7, no. 3, pp. 225–228, 1992. (in Chinese)
E. H. Mamdani, “Advances in the linguistic synthesis of fuzzy controllers,” International Journal of Man-Machine Studies, vol. 8, no. 6, pp. 669–678, 1976.
K. M. Passino and S. Yurkovich, Fuzzy Control, Reading, MA: Addison-Wesley, 1998.
Y. Tsukamoto, “An approach to fuzzy reasoning method,”, in Advances in Fuzzy Set Theory and Applications, Edited by M. M. Gupta, R. K. Ragade, and R. R. Yager, Amsterdam: North-Holland, 1979.
L. A. Zadeh, “Outline of a new approach to the analysis of complex systems and decision processes,” IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 1, pp. 28–44, 1973.
L. A. Zadeh, “The role of fuzzy logic in the management of uncertainty in expert systems,” Fuzzy Sets Systems, vol. 11, no. 1–3, pp. 197–198, 1983.
H. Zhang and L. Chen, “A technique for handling fuzzy decision-making problems concerning two kinds of uncertainty,” Cybernetics and Systems, vol. 22, no. 11, pp. 681–698, 1991.
H. Zhang and L. Chen, “A fuzzy decision-making technique with two kinds of uncertainty,” Science in China, Series A: Mathematics Physics Astronomy, vol. 34, no. 12, pp. 1508–1518, 1991.
H. Zhang and W. Xu, “Modern control theory applied to 200MW boiler: Turbine unit control,” Proceedings of the IFAC Symposium on Power Systems and Power Plant Control, Beijing, China, Aug. 1986, pp. 330–336.
H. J. Zimmermann, Fuzzy Set Theory and Its Applications, 4th Ed., Boston, MA: Kluwer Academic Publishers, 2001.
K. Zou and Y. Xu, Fuzzy Systems and Expert Systems, Sichuan, China: Southwest Jiaotong University Press, 1989.
R. Zwick, E. Carlstein, and D. V. Budescu, “Measures of similarity among fuzzy concepts: A comparative analysis,” International Journal of Approximate Reasoning, vol. 1, no. 2, pp. 221–242, Apr. 1987.
Rights and permissions
Copyright information
© 2006 Birkhäuser Boston
About this chapter
Cite this chapter
(2006). Fuzzy Inference and Control Methods Involving Two Kinds of Uncertainties. In: Fuzzy Modeling and Fuzzy Control. Control Engineering. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4539-7_6
Download citation
DOI: https://doi.org/10.1007/978-0-8176-4539-7_6
Publisher Name: Birkhäuser Boston
Print ISBN: 978-0-8176-4491-8
Online ISBN: 978-0-8176-4539-7
eBook Packages: EngineeringEngineering (R0)