Adaptive nonlinear system modeling using independent component analysis and neuro-fuzzy method
Sung-Soo Kim, Keun-Chang Kwak, Jeong-Woong Ryu, Bum-Jin Oh, Jun-Sik Hong · 2002
This paper represents a new approach to modeling a nonlinear system using the independent component analysis (ICA) and adaptive neuro-fuzzy inference system (ANFIS). To improve the performance of the model system, a set of inputs is transformed to be statistically independent using ICA as a preprocessing to the ANFIS established based on fuzzy c-means (FCM). The performance of the proposed method is demonstrated by applying it to the Box and Jenkins furnace data. The results of the computer simulation are demonstrated for the validity of this algorithm.