Pattern Classification Based on RBF Networks with Self-Constructing Clustering and Hybrid Learning

Radial basis function (RBF) networks are widely adopted to solve problems in the field of pattern classification. However, in the construction phase of such networks, there are several issues encountered, such as the determination of the number of nodes in the hidden layer, the form and initialization of the basis functions, and the learning of the parameters involved in the networks. In this paper, we present a novel approach for constructing RBF networks for pattern classification problems. An iterative self-constructing clustering algorithm is used to produce a desired number of clusters from the training data. Accordingly, the number of nodes in the hidden layer is determined. Basis functions are then formed, and their centers and deviations are initialized to be the centers and deviations of the corresponding clusters. Then, the parameters of the network are refined with a hybrid learning strategy, involving hyperbolic tangent sigmoid functions, steepest descent backpropagation, and least squares method. As a result, optimized RBF networks are obtained. With this approach, the number of nodes in the hidden layer is determined and basis functions are derived automatically, and higher classification rates can be achieved. Furthermore, the approach is applicable to construct RBF networks for solving both single-label and multi-label pattern classification problems.