An improved spectral conjugate gradient neural network training algorithm Print

I.E. Livieris and P. Pintelas, An Improved Spectral Conjugate Gradient Neural Network Training Algorithm, International Journal on Artificial Intelligence and Tools, 20(1), 2012.


Abstract - Conjugate gradient methods constitute excellent neural network training methods which are characterized by their simplicity and low memory requirements. In this paper, we propose a new spectral conjugate gradient method which guarantees the sufficient descent property using any line search. Moreover, we establish that our proposed method is globally convergent under the standard
Wolfe-Powell line search conditions. Experimental results provide evidence that our proposed method is preferable and in general
superior to the classical conjugate gradient methods in terms of efficiency and robustness.