Generalized Tagaki-Sugeno fuzzy rules based prediction model with application to power plant pulverizing system

This paper proposes a generalized Tagaki-Sugeno (TS) fuzzy rules based prediction model and apply it to estimate the pulverizing capability of ball mill pulverizing system of thermal power plant. The proposed method improves the core idea of the adaptive neuro-fuzzy inference system and does not use the neural network to interpret the model structure and the training process. Hence, the proposed model has generalization in a certain extent and could be applied efficiently on a variety of multi-variable and nonlinear dataset. For the proposed method, the Gaussian kernel fuzzy clustering algorithm is firstly used to determine the initial rules, and then the membership functions and the consequent parameters of TS fuzzy rules are tuned by the iterative optimization algorithm that minimizes the measure of the potential of data. The proposed model is performed on the field data obtained from a real thermal power plant and the experiments results verify the effectiveness of the proposed model.