Orthogonal least squares based center selection for fault-tolerant RBF networks

Abstract In the construction of a radial basis function (RBF) network, one of the most important issues is to determine the number of RBF nodes in the hidden layer and the corresponding centers. However, most existing center selection methods for RBF networks are designed for the faultless implementation situation only. In practice, the implementation of the network may be perturbed by faults and thus fault tolerance should be considered in selecting centers. This paper addresses this problem by focusing on the center selection problem of fault-tolerant RBF networks. In particular, we consider RBF networks under the concurrent fault situation and reformulate the fault-tolerant training objective function as a linear regression problem. A center selection and learning algorithm is then proposed based on the orthogonal least squares (OLS) algorithm. The proposed algorithm takes fault-tolerance into consideration both in center selection and network training, and it is able to determine the number of RBF nodes, the RBF centers and the trained connecting weights simultaneously. Simulation results show that the proposed algorithm is superior than state-of-the-art center selection algorithms.