DOI QR코드

DOI QR Code

Nonlinear Characteristics of Fuzzy Scatter Partition-Based Fuzzy Inference System

  • Park, Keon-Jun ;
  • Huang, Wei ;
  • Yu, C. ;
  • Kim, Yong K.
  • Received : 2013.01.23
  • Published : 2013.05.31

Abstract

This paper introduces the fuzzy scatter partition-based fuzzy inference system to construct the model for nonlinear process to analyze nonlinear characteristics. The fuzzy rules of fuzzy inference systems are generated by partitioning the input space in the scatter form using Fuzzy C-Means (FCM) clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the parameters of the consequence part are estimated by least square errors. The proposed model is evaluated with the performance using the data widely used in nonlinear process. Finally, this paper shows that the proposed model has the good result for high-dimension nonlinear process.

Keywords

Fuzzy Scatter Partition;Fuzzy Inference Systems;Fuzzy C-Means Clustering Algorithm;Rule Generation;Nonlinear Process

References

  1. J.S.R. Jang, Mizutani, E. and Sun, C.T.: Neuro-Fuzzy and Soft Computing, A Computational Approach to Learning and Machine Intelligence, Prentice Hall, NJ, (1997)
  2. L. A. Zadeh, Fuzzy sets, Information and Control, 8, 3, (1965)
  3. Y. Manai, and M. Benrejeb, New Condition of Stabilisation for Continuous Takagi-Sugeno Fuzzy System based on Fuzzy Lyapunov Function, International Journal of Control and Automation, 4, 3, (2011)
  4. K. J. Park, D. Y. Lee and J. P. Lee, Design of FCM-based fuzzy neural networks and its optimization for pattern recognition. Communications in Computer an Information Science, 261 CCIS, (2011)
  5. R. M. Tong, Synthesis of fuzzy models for industrial processes. Int. J Gen Syst. 4, (1978)
  6. W. Pedrycz, Numerical and application aspects of fuzzy relational equations. Fuzzy Sets Syst. 11, (1983)
  7. T. Takagi, and M. Sugeno, Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst, Cybern. SMC-15, 1, (1985)
  8. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, (1981)
  9. G.E.P., Box, G.M, Jenkins, Time Series Analysis: Forecasting and Control, 2nd ed., Holden-Day, San Francisco, CA (1976)