JUCS - Journal of Universal Computer Science 13(9): 1332-1343, doi: 10.3217/jucs-013-09-1332
Genetic Algorithm Based Recurrent Fuzzy Neural Network Modeling of Chemical Processes
expand article infoJili Tao‡, Ning Wang‡, Xuejun Wang§
‡ Zhejiang University, China§ Central South University, China

Corresponding author: Jili Tao ( jltao@iipc.zju.edu.cn )

© Jili Tao, Ning Wang, Xuejun Wang.
This article is freely available under the J.UCS Open Content License.
Citation: Tao J, Wang N, Wang X (2007) Genetic Algorithm Based Recurrent Fuzzy Neural Network Modeling of Chemical Processes. JUCS - Journal of Universal Computer Science 13(9): 1332-1343. https://doi.org/10.3217/jucs-013-09-1332
Open Access
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