Two Highly Efficient Second-Order Algorithms for Training Feedforward Networks  

Oleh:

Ampazis, N. ; Perantonis, S. J.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 13 no. 5 (2002), page 1064-1074.
Topik: TRAINING; efficient; algorithms; training; networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.7A Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We present two highly efficient second - order algorithms for the training of multilayer feedforward neural networks. The algorithms are based on iterations of the form employed in the Levenberg - Marquardt (LM) method for non linear least squares problems with the inclusion of an additional adaptive momentum term arising from the formulation of the training task as a constrained optimization problem. Their implementation requires minimal additional computations compared to a standard LM iteration. Simulations of large scale classical neural - network benchmarks are presented which reveal the power of the two methods to obtain solutions in difficult problems, whereas other standard second - order techniques (including LM) fail to converge.

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