New Second-order Learning Algorithm for Neural Networks and Its Application

A new second order recursive learning algorithm is presented. The algorithm is equivalent to Newton iterative method and has second order convergent speed. It achieves the recurrence calculation of Newton search divections and the inverse of Hessian matrices. The analysis and application results show the efficiency of the new algorithm.