Performance Functions Alternatives of Mse for Neural Networks Learning

Recently multilayer feed-forward neural networks are often used in several fields, as industrial modeling, universal function approximations, and as classifiers. These supervised neural networks are commonly trained by a traditional backpropagation learning algorithm, which minimizes the mean squared error (Mse) of the training data. All previous efforts has been exerted to find alternatives of Mse in the presence of outliers (noisy data), however Mse is not robust in presence of outliers that may be pollute the training data. For first time we aim in our paper to present M-Estimators as performance functions alternatives of Mse Performance function in the case of using high quality clean data. We compared between Mse and M-estimators in two applications crab classification, and function approximation.