Robust back propagation learning algorithm based on near sets

The traditional robust learning algorithms are based on the estimated errors, which is not correct in the early stage of the training process. Therefore, the use of those approaches still cannot provide very decent learning performance in face of outliers unless a set of good initial weights is used. In this paper, a novel approach, termed as NRBP (Near set based Robust Back Propagation learning algorithm) is proposed. In this learning algorithm, the training (estimated) data sets are separated into overlapping (or nonoverlapping) subsets of those data. It uses the set error measure instead of one-step error in robust back propagation based on near set. The set error measure is an estimated error measure between a subset of training data set and corresponding subset of estimated data set. Its benefit is it includes error messages and also reduces the outlier effect.