Genetic Algorithm Based Recurrent Fuzzy Neural Network Modeling of Chemical Processes
Дата
Авторы
Tao,Jili
Wang,Ning
Wang,Xuejun
Journal Title
Journal ISSN
Volume Title
Издатель
Journal of Universal Computer Science
Аннотация
Описание
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.
Ключевые слова
genetic algorithm , recurrent fuzzy neural network , modeling , PH neutralization process