Affiliations: Departamento de Computacion, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico | Departamento de Control Automatico, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico | School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
Note: [] Corresponding author. Wen Yu, Departamento de Control Automatico, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, 07360, Mexico. Tel.: +52 55 57473734, E-mail: [email protected]
Abstract: Unlike the other fuzzy neural networks, in this paper the fuzzy system and the neural network are separated, and they are corresponding to structure identification and parameter identification. The fuzzy model is generated automatically by on-line clustering method and fuzzy support vector machines. This fuzzy model is not updated, but its modeling error is compensated by a neural network. The neural network acts as the parameter identification model. The benefits of this new method are the structure is simple, and the physical meanings of each part of the model are clear. For the neural compensator, a new stable and fast training algorithm is proposed. Finally, this modeling method is successfully applied to model a magnetic tube recovery ratio in a metal company in China.
