Title :
Generalized fuzzy RBF networks and nonlinear system identifications
Author :
Hong, Bao ; Yun, Xie ; Xinkuo, Chen
Author_Institution :
Fac. of Autom., Guangdong Univ. of Technol., Guangzhou, China
Abstract :
Based on summing up three kinds of fuzzy inference systems and the functional equivalence between the radial basis function (RBF) networks and fuzzy inference systems, the paper presents a new concept of generalized fuzzy inference and the new model of generalized fuzzy RBF network. Then the generalized learning algorithm is derived. A nonlinear system identification is done by this network. Results have verified that the generalized fuzzy RBF networks have an ability to approximate arbitrary nonlinear function with an arbitrary given accuracy and the learning algorithm described in the paper is effective and available.
Keywords :
fuzzy logic; fuzzy neural nets; identification; inference mechanisms; learning (artificial intelligence); nonlinear systems; radial basis function networks; arbitrary nonlinear function; functional equivalence; fuzzy inference systems; generalized fuzzy RBF networks; generalized fuzzy inference; generalized learning algorithm; nonlinear system identifications; radial basis function networks; Automation; Equations; Fault diagnosis; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Inference algorithms; Nonlinear systems; Radial basis function networks;
Conference_Titel :
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
Print_ISBN :
0-7803-7268-9
DOI :
10.1109/WCICA.2002.1021546
