Conjugate gradient methods constitute excellent neural network training methods characterized by their simplicity efficiency and their very low memory requirements. In this paper, we propose a new scaled conjugate gradient neural network training algorithm which guarantees descent property with standard Wolfe condition. Encouraging numerical experiments verify that the proposed algorithm provides fast and stable convergence.
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