Noisy time series prediction using M-estimator based robust radial basis function neural networks with growing and pruning techniques

In this paper, we present an M-estimator based robust radial basis function (RBF) learning algorithm with growing and pruning techniques. The Welsch M-estimator and median scale estimator are employed to avoid the influence from outliers. The concept of neuron significance is adopted to implement the growing and pruning techniques of network nodes. The proposed method not only eliminates the influence of the outliers, but also dynamically adjusts the number of neurons to approach an appropriate size of the network. The results from experiments show that the proposed method can give a minimum prediction error compared with other methods. Furthermore, even 30% of all observations are the outliers this method still has a good performance.