Title :
Size-reducing RBF networks
Author :
Murata, Junichi ; Itoh, Shinji ; Hirasawa, Kotaro
Author_Institution :
Kyushu Univ., Fukuoka, Japan
Abstract :
An approach is proposed to reduce the complexity of radial basis function (RBF) networks. This approach starts with enough hidden nodes and reduces the number of nodes in the course of learning. The algorithm can be employed in problems where only the performance index of the network output is given, as well as in the supervised training problems where the desired output values are available. Also, it is applicable to classification problems and function approximation problems
Keywords :
function approximation; generalisation (artificial intelligence); learning (artificial intelligence); least squares approximations; parameter estimation; pattern classification; radial basis function networks; classification problems; function approximation problems; network performance index; size reduction; supervised training problems; Algorithm design and analysis; Clustering algorithms; Function approximation; Genetic algorithms; Humans; Least squares methods; Performance analysis; Radial basis function networks; Training data;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.831151
