An Algorithm to Determine RBFNN’s Center Based on the Improved Density Method

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.

Cite this paper

M. Zheng and Y. Zhang, "An Algorithm to Determine RBFNN’s Center Based on the Improved Density Method," Open Journal of Applied Sciences, Vol. 4 No. 1, 2014, pp. 1-5. doi: 10.4236/ojapps.2014.41001.

Conflicts of Interest

The authors declare no conflicts of interest.

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