A Hybrid Linear / Nonlinear Training Algorithm for Feedforward Neural Networks  

Oleh:

Lightbody, G. ; McLoone, S. ; Irwin, G. ; Brown, M. D.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 9 no. 4 (1998), page 669-684.
Topik: TRAINING; hybrid linear; training algorithm; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.3 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This paper presents a new hybrid optimization strategy for training feedforward neural networks. The algorithm combines gradient-based optimization of nonlinear weights with singular value decomposition (SVD) computation of linear weights in one integrated routine. It is described for the multilayer perceptron (MLP) and radial basis function (RBF) networks and then extended to the local model network (LMN), a new feedforward structure in which a global nonlinear model is constructed from a set of locally valid submodels. Simulation results are presented demonstrating the superiority of the new hybrid training scheme compared to second - order gradient methods. It is particularly effective for the LMN architecture where the linear to non linear parameter ratio is large.

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