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Characteristics of Fuzzy Inference Systems by Means of Partition of Input Spaces in Nonlinear Process

비선형 공정에서의 입력 공간 분할에 의한 퍼지 추론 시스템의 특성 분석

  • 박건준 (수원대학교 전기공학과) ;
  • 이동윤 (중부대학교 전기전자공학과)
  • Received : 2010.11.16
  • Accepted : 2011.01.13
  • Published : 2011.03.28

Abstract

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

Keywords

Fuzzy Inference Systems;Partition of Input Space;Min-Max Method;C-Means Clustering;Characteristics of Nonlinear Process

References

  1. R. M. Tong, "Synthesis of fuzzy models for industrial processes," Int. J. Gen. Syst., Vol.4, pp.143-162, 1978. https://doi.org/10.1080/03081077808960680
  2. W. Pedrycz, "An identification algorithm in fuzzy relational system," Fuzzy Sets Syst., Vol.13, pp.153-167, 1984. https://doi.org/10.1016/0165-0114(84)90015-0
  3. W. Pedrycz, "Numerical and application aspects of fuzzy relational equations," Fuzzy Sets Syst., Vol.11, pp.1-18, 1983. https://doi.org/10.1016/S0165-0114(83)80066-9
  4. E. Czogola and W. Pedrycz, "On identification in fuzzy systems and its applications in control problems," Fuzzy Sets Syst., Vol.6, pp.73-83, 1981. https://doi.org/10.1016/0165-0114(81)90081-6
  5. C. W. Xu, "Fuzzy system identification," IEEE Proceeding Vol.126, No.4, pp.146-150, 1989.
  6. R. M. Tong, "The evaluation of fuzzy models derived from experimental data," Fuzzy Sets Syst., Vol.13, pp.1-12, 1980.
  7. C. W. Xu and Y. Zailu, "Fuzzy model identification self-learning for dynamic system," IEEE Trans. on Syst. Man, Cybern., Vol.SMC-17, No.4, pp.683-689, 1987. https://doi.org/10.1109/TSMC.1987.289361
  8. Box and Jenkins, "Time Series Analysis, Forcasting and Control," Holden Day, SanFrancisco, CA.
  9. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Trans. Syst. Cybern., Vol.SMC-15, No.1, pp.116-132, 1985. https://doi.org/10.1109/TSMC.1985.6313399
  10. M. A. Ismail, "Soft Clustering Algorithm and Validity of Solutions," Fuzzy Computing Theory, Hardware and Applications, edited by M.M. Gupta, North Holland, pp.445-471, 1988.
  11. P. R. Krishnaiah and L. N. Kanal, editors. Classification, pattern recognition, and reduction of dimensionality, volume 2 of Handbook of Statistics. North-Holland, Amsterdam, 1982.

Cited by

  1. Nonlinear Characteristics of Fuzzy Inference Systems by Means of Individual Input Space vol.12, pp.11, 2011, https://doi.org/10.5762/KAIS.2011.12.11.5164
  2. Characteristics of Gas Furnace Process by Means of Partition of Input Spaces in Trapezoid-type Function vol.12, pp.4, 2014, https://doi.org/10.14400/JDC.2014.12.4.277