Abstract
A genetic algorithm (GA) based recurrent fuzzy neural network modeling method for dynamic nonlinear chemical process is presented. The dynamic recurrent fuzzy neural network (RFNN) is constructed in terms of Takagi-Sugeno fuzzy model. The consequent part is comprised of the dynamic neurons with output feedback. The number and the parameters of membership functions in the premise part are optimized by the GA considering both the approximation capability and structure complexity of RFNN. The proposed dynamic model is applied to a PH neutralization process and the advantages of the resulting model are demonstrated.
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