Mohammad Mokhtare - Faculty of Eng., Mechatronics Dept., Science and Research Branch, Islamic Azad University
Somayeh Hekmati Vahed
Mahdi Aliyari Shoorehdeli - Faculty of Electrical Engineering, Mechatronics Dept., K. N. Toosi University of Tech
Alireza Fatehi - Faculty of Electrical Engineering, Mechatronics Dept., K. N. Toosi University of Tech

This paper presents a Neuro-fuzzy based method using local linear model trees (LOLIMOT) train algorithm for nonlinear identification of a catalytic reformer unit in oil refinery plant. This unit include highly nonlinear behaviour and it is complicated to obtain an accurate physical model. There for, it is necessary to use such appropriate method providing suitable while preventing computational complexities. LOLIMOT algorithm as an incremental learning algorithm has been used several time as a well-known method for nonlinear system identification and estimation. For comparison, Multi Layer Perceptron (MLP) and Radial Bases Function (RBF) neural networks as well-known methods for nonlinear system identification and estimation are used to evaluate the performance of LOLIMOT. The results presented in this paper clearly demonstrate that the LOLIMOT is superior to other methods in identification of nonlinear system such as catalytic reformer unit (CRU

Locally Linear Model Tree, Nonlinear Identification, Catalytic Reformer Unit