A probabilistic fuzzy logic system: Learning in the stochastic environment with incomplete dynamics

A completely new type of fuzzy logic system will be developed from the existing fuzzy structure and applied to modeling and control of complex processes under incomplete dynamics in the manufacturing industry. Using a unique three-dimensional membership function (fuzz grade, time and probability), the probabilistic processing features can be added into the existing fuzzy configuration to construct a probabilistic fuzzy inference engine. Thus, this developed probabilistic fuzzy logic system (PFLS) is able to learn uncertain information in both fuzzy and stochastic nature. The proposed PFLS will be very suitable to modeling of the complex stochastic process with incomplete dynamics. All the existing learning theories and methods can be directly applied to the proposed PFLS to enhance its learning performance. Integrated into the fuzzy-PID structure, it will turn into a probabilistic fuzzy logic controller for the stochastic control. Successful application of the proposed PLFS to the selected industrial process will have a great impact on both academia and industry. ©2009 IEEE.