Effective learning system techniques for human-robot interaction in service environment
Knowledge-Based Systems
Probabilistic fuzzy logic system: a tool to process stochastic and imprecise information
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Probabilistic-constrained fuzzy logic for situation modeling
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
An extended fuzzy logic system for uncertainty modelling
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
A probabilistic fuzzy logic system: learning in the stochastic environment with incomplete dynamics
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
An interval fuzzy controller for vehicle active suspension systems
IEEE Transactions on Intelligent Transportation Systems
A probabilistic fuzzy approach to modeling nonlinear systems
Neurocomputing
A hierarchical structure of observer-based adaptive fuzzy-neural controller for MIMO systems
Fuzzy Sets and Systems
Probabilistic support vector machines for classification of noise affected data
Information Sciences: an International Journal
The distance of probabilistic fuzzy sets for classification
Pattern Recognition Letters
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In this paper, a probabilistic fuzzy logic system (PFLS) is proposed for the modeling and control problems. Similar to the ordinary fuzzy logic system (FLS), the PFLS consists of the fuzzification, inference engine and defuzzification operation to process the fuzzy information. Different to the FLS, it uses the probabilistic modeling method to improve the stochastic modeling capability. By using a three-dimensional membership function (MF), the PFLS is able to handle the effect of random noise and stochastic uncertainties existing in the process. A unique defuzzification method is proposed to simplify the complex operation. Finally, the proposed PFLS is applied to a function approximation problem and a robotic system. It shows a better performance than an ordinary FLS in stochastic circumstance.