Title :
Structural learning of recurrent RBF networks with M-apoptosis
Author :
Honda, Katsuhiro ; Miyoshi, Tetsuya ; Ichihashi, Hidetomo
Author_Institution :
Osaka Prefectural Univ., Sakai, Japan
Abstract :
The apoptosis is an active form of cell death which plays an important role during embryonic development. We propose a unified approach, called M-apoptosis, to the structural learning of recurrent RBF networks. Minkowski norm of the first order derivatives of RBF networks with respect to input variables is added to the cost function for determining the unknown parameters. The parameters are changed so that the MSE and the first order derivatives become small during the learning process. After learning, the input variables of the units with small first order derivatives are deleted
Keywords :
feedforward neural nets; learning (artificial intelligence); recurrent neural nets; Minkowski norm; RBF neural networks; apoptosis; cost function; first order derivatives; recurrent neural networks; structural learning; Biological neural networks; Computer architecture; Cost function; Embryo; Equations; Input variables; Neural networks; Radial basis function networks; Recurrent neural networks; Training data;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.687236
