Title :
An improved learning algorithm of RBF networks
Author_Institution :
Coll. of Comput. Sci. & Technol., Changchun Univ., Changchun, China
Abstract :
In this paper the systematic analysis and research has been made to the various learning methods of RBFNN (Radial Basis Function Neural Network). The clustering method is introduced to RBFNN. Some main methods are combined to enhance the performance in some degree. Then the improved GLVQ center learning method is put forward on the basis of ERPCL and GLVQ. Finally the GLVQ-RGLS method is the union of the above two methods. Simulation results show that the method has a good learning performance.
Keywords :
learning (artificial intelligence); pattern clustering; radial basis function networks; GLVQ center learning method; RBF networks; RBFNN; clustering method; learning algorithm; radial basis function neural network; Artificial neural networks; Inference algorithms; Learning systems; Least squares approximation; Radial basis function networks; Simulation; Vector quantization; DAC; ERPCL; GLVQ-RGLS; IMC;
Conference_Titel :
Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on
Conference_Location :
Deng Leng
Print_ISBN :
978-1-4577-0535-9
DOI :
10.1109/AIMSEC.2011.6010677
