An Efficient Configuration for Probabilistic Fuzzy Logic System

A novel inference configuration is proposed to improve the computational efficiency and information loss in the probabilistic fuzzy inference process. The probabilistic inference and the fuzzy inference are unified in one operation based on the continuous form of the probabilistic fuzzy set. Besides the faster inference operation, it is able to produce fuzzy outputs in a complete probabilistic distribution that in turn will provide information about the approximation bound. The computational analyses of six different fuzzy systems demonstrate the inference efficiency of the proposed method. Its effectiveness can be further demonstrated on the application to modeling of an industrial curing process. The robust modeling performance discloses its potential in process modeling under complex environment.