Abstract
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.
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