Title of article
Genetic Algorithm Based Recurrent Fuzzy Neural Network Modeling of Chemical Processes
Author/Authors
Tao, Jili Zhejiang University - Institute of Advanced Process Control - National Laboratory of Industrial Control Technology, China , Wang, Ning Zhejiang University - Institute of Advanced Process Control - National Laboratory of Industrial Control Technology, China , Wang, Xuejun Central South University - Department of Information Science and Engineering, China
From page
1332
To page
1343
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.
Keywords
Genetic algorithm , Recurrent fuzzy neural network , Modeling , PH neutralization process
Journal title
Journal of J.UCS (Journal of Universal Computer Science)
Journal title
Journal of J.UCS (Journal of Universal Computer Science)
Record number
2660854
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