Title :
Identification of Nonlinear System Based on ANFIS with Subtractive Clustering
Author :
Yue, Junhong ; Liu, Jizhen ; Liu, Xiangjie ; Tan, Wen
Author_Institution :
Dept. of Autom., North China Electr. Power Univ., Beijing
Abstract :
Subtractive clustering is used to generate an initial T-S fuzzy model with the appropriate rule number and performance index by adjusting the radius of a cluster center. To acquire a T-S fuzzy model with perfect performance, adaptive neuro-fuzzy inference system (ANFIS) is combined to fine tune the premise parameters and consequent parameters by means of a hybrid gradient descent (GD) and least-squares estimation (LSE). A simulation to a dynamic nonlinear system demonstrates the effective of this method
Keywords :
adaptive systems; fuzzy reasoning; gradient methods; identification; least squares approximations; neural nets; nonlinear systems; pattern clustering; T-S fuzzy model; adaptive neuro-fuzzy inference system; gradient descent; least-squares estimation; nonlinear system identification; subtractive clustering; Adaptive systems; Automation; Electronic mail; Fuzzy systems; Least squares approximation; Nonlinear dynamical systems; Nonlinear systems; Performance analysis; Power generation; Power system modeling; Adaptive nuero-fuzzy inference system; T-S fuzzy model; radius of a cluster center; subtractive clustering;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1712675
