Training feedforward neural networks with the Dogleg method and BFGS Hessian updates
Stavros J. Perantonis, Nicholas Ampazis, S. Spirou · 2000
We introduce an advanced optimization algorithm for training feedforward neural networks. The algorithm combines the Broyden-Fletcher-Goldfarb-Shanno (BFGS) Hessian update formula with a special case of trust region techniques, called the Dogleg method, as an alternative technique to line search methods. Simulations regarding classification and function approximation problems are presented which reveal a clear improvement both in convergence and success rates over standard BFGS implementations.