A probabilistic fuzzy logic system for uncertainty modeling

A probabilistic fuzzy logic system (PFLS) is proposed for the modeling and control problems. In addition to the traditional fuzzification, inference engine and defuzzification operation for processing fuzzy information, it uses the probabilistic modeling method to improve the stochastic modeling capability. With a proper three-dimensional membership function, the PFLS could be designed to handle the effect of random noise and stochastic uncertainties in the modeling process. The simulation result shows that the proposed PFLS can treat the uncertainty modeling problem well.

[1]  John W. Seaman,et al.  Unity and diversity of fuzziness-from a probability viewpoint , 1994, IEEE Trans. Fuzzy Syst..

[2]  Zhi Liu,et al.  Fuzzy neural network quadratic stabilization output feedback control for biped robots via H/sub /spl infin// approach. , 2003, IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society.

[3]  Fengming Song,et al.  What does a probabilistic interpretation of fuzzy sets mean? , 1996, IEEE Trans. Fuzzy Syst..

[4]  Ronald R. Yager,et al.  Fuzzy modeling for intelligent decision making under uncertainty , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[5]  Ana Colubi,et al.  Simulation of random fuzzy variables: an empirical approach to statistical/probabilistic studies with fuzzy experimental data , 2002, IEEE Trans. Fuzzy Syst..

[6]  Kimon P. Valavanis,et al.  Probabilistic modeling of intelligent robotic systems , 1991, IEEE Trans. Robotics Autom..

[7]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[8]  Jerry M. Mendel,et al.  Type-2 fuzzy logic systems , 1999, IEEE Trans. Fuzzy Syst..

[9]  Jih-Gau Juang,et al.  Fuzzy neural network approaches for robotic gait synthesis , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Mohammad R. Akbarzadeh-Totonchi,et al.  Probabilistic fuzzy logic and probabilistic fuzzy systems , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[11]  J. C. Pidre,et al.  Probabilistic model for mechanical power fluctuations in asynchronous wind parks , 2003 .

[12]  L. Zadeh.  Discussion: probability theory and fuzzy logic are complementary rather than competitive , 1995 .

[13]  Carlos Canudas de Wit,et al.  Theory of Robot Control , 1996 .

[14]  Ma Songde,et al.  A new radial basis probabilistic neural network model , 1996, Proceedings of Third International Conference on Signal Processing (ICSP'96).

[15]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[16]  Kazuo Tanaka,et al.  Fuzzy control systems design and analysis , 2001 .