Constrained PSO Based Center Selection for RBF Networks Under Concurrent Fault Situation

In the training of radial basis function (RBF) networks, one important issue is to select RBF centers before constructing the networks. Most existing center selection methods are designed for the fault-free situation only. However, as the implementation of the networks may be perturbed by faults, these algorithms may lead to networks with degraded performance. This paper considers the center selection problem for RBF networks under the concurrent fault situation where multiplicative weight noise and open weight fault exist simultaneously. In particular, we introduce a binary label vector indicating the centers selected from training samples. Using the label vector, the fault-tolerant RBF model under the concurrent fault situation is reformulated as a constrained optimization problem, so that fault-tolerance can be considered in the procedure of center selection. To solve this constrained optimization problem, a constrained particle swarm optimization based algorithm is developed to select centers and train the network simultaneously. Simulation results show that the proposed algorithm is superior than state-of-the-art center selection algorithms.