Wettschereck, D. and Dietterich, T. (1992) Improving the performance of radial basis function networks by learning center locations. Moody, J., Hanson, S. and Lippman R., Eds., Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers, San Mateo.

has been cited by the following article:

• 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.