Neural Network for Data Clustering Based on RBF with

Potential Function Generators

Project Goals

• Development of new strategy of shape-adaptive radial basis functions based on potential functions and optimization procedure for positioning of the cluster centers during the learning process.

• Development of a three-layer RBF neural network architecture with symmetric potential functions and criteria for merging and splitting the clusters. Analysis of how the shape of the clusters changes with dimension of the input data, choice of symmetric potential function and the number of centers.

• Analysis of how the shape of the clusters changes with dimension of the input data, choice of symmetric potential function and the number of centers.

• We will investigate the dependence of our method on these parameters and apply it to several artificial and benchmark data sets in order to study the power of the PFEs in generating classification solutions for various shapes of teaching patterns that are robust with respect to noise in the data.

• Comparison of results with some standard methods for clustering. Several benchmark data sets will be considered to show the clustering performance on the training and test sets achieved by the proposed approach and some other neural network models.