An Experimental Nonlinear System Identification Based on Local Linear Neuro-Fuzzy Models

This paper presents a neuro-fuzzy based method using local linear model trees (LOLIMOT) train algorithm for nonlinear identification of a temperature control pilot plant. Such systems include highly nonlinear behavior and it is complicated to obtain an accurate physical model. Therefore, it is necessary to use such appropriate tools providing suitable models while preventing computational complexities. The identification results of pilot plant confirm the high performance of proposed method in two operational modes.