Abstract
A neuro-fuzzy network predictive approach is introduced to design a control system for boiler steam temperature process. While the nonlinear process is modeled by neuro-fuzzy technique containing local CARIMA model, traditional generalized predictive control method can be extended to nonlinear case in a neuro-fuzzy fashion. The proposed method was found to provide a satisfactory performance over the 200MW superheater steam temperature system.
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