Hybrid fuzzy modeling of chemical processes

Abstract

Fuzzy models have been proved to have the ability of modeling all plants without any priori information. However, the performance of conventional fuzzy models can be very poor in the case of insufficient training data due to their poor extrapolation capacity. In order to overcome this problem, a hybrid grey-box fuzzy modeling approach is proposed in this paper to combine expert experience, local linear models and historical data into a uniform framework. It consists of two layers. The expert fuzzy model constructed from linguistic information, the local linear model and the T-S type fuzzy model constructed from data are all put in the first layer. Layer 2 is a fuzzy decision module that is used to decide which model in the first layer should be employed to make the final prediction. The output of the second layer is~the output of the hybrid fuzzy model. With the help of the linguistic information, the poor extrapolation capacity problem caused by sparse training data for conventional fuzzy models can be overcome. Simulation result for pH neutralization process demonstrates its modeling ability over the linear models, the expert fuzzy model and the conventional fuzzy model.