A fast learning algorithm of feedforward neural networks by using novel error functions

This paper presents two novel alternative families of error functions as the generalized training criterion of feedforward neural networks; they can significantly accelerate the convergence rate in the midterm and the last training stages. Their training speed is faster than the original fast backpropagation algorithm by parameter optimization. Several approaches to parameter optimization are explored and verified by experiments.