Parallel Nonlinear Optimization Techniques for Training Neural Networks  

Oleh:

Phua, P. K. H. ; Ming, Daohua

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 6 (Nov. 2003), page 1460-1468.
Topik: non linear; parallel; non linear; optimization techniques; training; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.9 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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.

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