A Probabilistic Neural-Fuzzy Learning System for Stochastic Modeling

A probabilistic fuzzy neural network (PFNN) with a hybrid learning mechanism is proposed to handle complex stochastic uncertainties. Fuzzy logic systems (FLSs) are well known for vagueness processing. Embedded with the probabilistic method, an FLS will possess the capability to capture stochastic uncertainties. Further enhanced with the neural learning, it will be able to work under time-varying stochastic environment. Integrated with a statistical process control (SPC) based monitoring method, the PFNN can maintain the robust modeling performance. Finally, the successful simulation demonstrates the modeling effectiveness of the proposed PFNN under the time-varying stochastic conditions.