Comparative analysis of fuzzy inference systems implemented on neural structures

This paper presents comparative analysis of two popular neural fuzzy inference systems, namely, fuzzy adaptive learning control/decision network (FALCON) and adaptive network based fuzzy inference system (ANFIS), and their application to an induction motor fault detection problem. The fault detectors are analyzed with respect to architectural and fuzzy inference system specifications, and the results for motor fault detection are evaluated in terms of fault detection accuracy, knowledge extraction capability, and computational complexity. The advantages and disadvantages of using these two architectures are also discussed. The experimental results suggest a promising future for using neural fuzzy inference systems for incipient fault detection in induction motors