A Histogram Modification Approach for Analysis of Membership Function Relocation in Fuzzy Logic Control
Jia Lu and Yunxia Hu
Department of Information System, University of Phoenix, 5050 NW 125 Avenue, Coral Springs, FL 33076, USA
A histogram modification approach was proposed for the analysis of membership function relocation. We employed this approach to explicitly analyze and exploit the accurate approximation of the error and change in error input for the membership functions. The paper integrated knowledge-based fuzzy control rule with the histogram modification methods to analyze the control spatial distribution of the membership functions with the intervals for the error and change in error in real time error histogram. For this research, we also described the principle of design and sufficient theory analysis methods. The simulations were provided for the illustration and analysis on the extensive control operations for the effectiveness of our approach.
-  G. M. Abdelnour, C. Change, Y. Huang, and J. Y. Cheung, “Design of a Fuzzy Controller Using Input and Output Mapping Factors,” IEEE Trans. On Systems, Man and Cybernetics, Vol.21, No.5, pp. 952-960, 1991.
-  Y. Bai and P. Khedkar, “Learning and Tuning Fuzzy Logic Controllers Through Reinforement,” IEEE Trans. On Systems, Man and Cybernetics, Vol.10, pp. 120-124, 2000.
-  D. Driankov and M. Reinfrank, “An introduction to fuzzy control,” Second Edition, Springer-Verlag, Berlin, 1996.
-  F. Herrea, H. Lozano, and J. L. Verdegay, “Generating Fuzzy Rules from Examples Using Genetic Algorithm,” Fuzzy logic and Soft Computing, B. Bouchon-Meunier, et al. (Ed.), World Scientific, pp. 11-20, 1995.
-  Y. F. Li and C. C. Lau, “Development of Fuzzy Algorithm for Servo Systems,” IEEE International Conference on Robotics and Automation, Philadelphia, April 24-29, 1988.
-  G. C. H. Wang and S. C. Lin, “A Stability Approach to Fuzzy Control Design Nonlinear Systems,” International Journals for Fuzzy Sets and Systems, Vol.48, pp. 279-287, 1992.
-  S. Chaudhuri, “Self-tuning Histograms: Building Histograms Without Looking at Data,” SIGMOD, pp. 181-192, 1999.
-  M. J. Carlotto, “Histogram Analysis Using a Scale-space Approach,” IEEE PAMI, Vol.9, No.1, pp. 121-129, 1987.
-  Y. Shi and M. Mizumoto, “A new approach of neuro-fuzzy learning algorithm for tuning fuzzy rules,” Fuzzy Sets and Systems, Vol.112, No.1, pp. 99-116, 2000.
-  S. J. Go, M. C. Lee, and M. K. Park, “Design of Fuzzy-Sliding Mode Control with the Self Tuning Fuzzy Inference Based on Genetic Algorithm and Its Application,” Transactions on Control Automation and Systems Engineering, Vol.3, No.1, pp. 58-65, 2001.
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