A Preconditioned Unconstrained Optimization Method for Training Multilayer Feed-forward Neural Network

Year 2019, Volume: 2 Issue: 2, 71 - 79, 24.02.2020

Khalil Abbo Zahra Zahra Abdlkareem

Abstract

Non-linear unconstrained
optimization  methods constitute
excellent neural network training methods characterized by their simplicity and
efficiency. In this paper, we propose a new preconditioned 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.

Keywords

Unconstrained optimization, Neural network, Descent property

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