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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Zenodo (CERN European Organization for Nuclear Research)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.