Second-order learning methods for a multilayer perceptron

First-and second-order learning methods for feed-forward multilayer networks are studied. Newtontype and quasi-Newton algorithms are considered and compared with commonly used backpropagation algorithm. It is shown that, although second-order algorithms reguire enhanced computer facilities, they provide better convergence and simplicity in usage.