TITLE:
An Algorithm to Determine RBFNN’s Center Based on the Improved Density Method
AUTHORS:
Mingwen Zheng, Yanping Zhang
KEYWORDS:
Radial Basis Function Neural Network; Data Center; Expansion Constant; Density Method; Full-Supervised Algorithm
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.4 No.1,
January
10,
2014
ABSTRACT: It takes more time and is easier to fall into
the local minimum value when using the traditional
full-supervised learning algorithm to train RBFNN. Therefore, the paper proposes one algorithm to determine the RBFNN’s data center based on the
improvement density method. First it uses the improved density method to select
RBFNN’s data center, and calculates the expansion constant of each center, then
only trains the network weight with the gradient descent method. To compare
this method with full-supervised gradient descent method, the time not only has
obvious reduction (including to choose data center’s time by density method),
but also obtains better classification results when using the data set in UCI to carry on the test to the network.