Parallel nonlinear optimization techniques for training neural networks

Authors:
P. K.H. Phua;Daohua Ming
Affiliations:
Dept. of Comput. Sci., Nat. Univ. of Singapore, Singapore;-
Venue:
IEEE Transactions on Neural Networks
Year:
2003

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Abstract

In this paper, we propose the use of parallel quasi-Newton (QN) optimization techniques to improve the rate of convergence of the training process for neural networks. The parallel algorithms are developed by using the self-scaling quasi-Newton (SSQN) methods. At the beginning of each iteration, a set of parallel search directions is generated. Each of these directions is selectively chosen from a representative class of QN methods. Inexact line searches are then carried out to estimate the minimum point along each search direction. The proposed parallel algorithms are tested over a set of nine benchmark problems. Computational results show that the proposed algorithms outperform other existing methods, which are evaluated over the same set of test problems.