Two Algorithms for Neural-Network Design and Training With Application to Channel Equalization  

Oleh:

Mulgrew, B. ; Gibson, G. J. ; Sweatman, C. Z. W. H.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 9 no. 3 (1998), page 533-543.
Topik: educational equalization; two algorithms; neural - network design; training; channel equalization

Ketersediaan

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

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Isi artikel

We describe two algorithms for designing and training neural - network classifiers. The first, the linear programming slab algorithm (LPSA), is motivated by the problem of reconstructing digital signals corrupted by passage through a dispersive channel and by additive noise. It constructs a multilayer perceptron (MLP) to separate two disjoint sets by using linear programming methods to identify network parameters. The second, the perceptron learning slab algorithm (PLSA), avoids the computational costs of linear programming by using an error - correction approach to identify parameters. Both algorithms operate in highly constrained parameter spaces and are able to exploit symmetry in the classification problem. Using these algorithms, we develop a number of procedures for the adaptive equalization of a complex linear 4 - quadrature amplitude modulation (QAM) channel, and compare their performance in a simulation study. Results are given for both stationary and time - varying channels, the latter based on the COST 207 GSM propagation model.

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