Neural Networks for Data Clusterization Based on

Potential Function Generators

Student: Kevin Mahoney

Project Goals

The objectives of this project are:

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

• Development of learning algorithm, which includes static (fixed number n of radial basis functions) and dynamic (during the computation is able to add or delete one or more radial basis functions) phases.

• Development of a three-layer RBF neural network architecture with symmetric potential functions and modified competitive Hebbian learning in order to maintain the neighborhood topology.

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

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