A recurrent Newton algorithm and its convergence properties
Chung‐Ming Kuan · IEEE Transactions on Neural Networks · 1995
In this paper a recurrent Newton algorithm for an important class of recurrent neural networks is introduced. It is noted that a suitable constraint must be imposed on recurrent variables to ensure proper convergence behavior. The simulation results show that the proposed Newton algorithm with the suggested constraint performs uniformly better than the backpropagation algorithm and the Newton algorithm without the constraint, in terms of mean-squared errors.