Title :
Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks
Author :
Chen, S. ; Wu, Y. ; Luk, B.L.
Author_Institution :
Dept. of Electr. & Comput. Sci., Southampton Univ., UK
fDate :
9/1/1999 12:00:00 AM
Abstract :
Presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and prediction is used as an example to demonstrate the effectiveness of this hierarchical learning approach
Keywords :
genetic algorithms; learning (artificial intelligence); least squares approximations; radial basis function networks; time series; genetic algorithm optimization; hierarchical learning approach; nonlinear time series modeling; regularization parameter; regularized orthogonal least squares learning; two-level learning method; Computational efficiency; Cost function; Genetic algorithms; Helium; Learning systems; Least squares methods; Network topology; Neural networks; Predictive models; Radial basis function networks;
Journal_Title :
Neural Networks, IEEE Transactions on
