Recursive Least-Squares Backpropagation Algorithm for Stop-and-Go Decision-Directed Blind Equalization  

Oleh:

Zerguine, A. ; Bettayeb, M. ; Abrar, S.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 13 no. 6 (2002), page 1472-1481.
Topik: BACKPROPAGATION; recursive; backpropagation; algorithms; decision; equalization

Ketersediaan

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

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Isi artikel

Stop - and - go decision - directed (S & G - DD) equalization is the most primitive blind equalization (BE) method for the cancelling of intersymbol - interference in data communication systems. Recently, this scheme has been applied to complex-valued multilayer feedforward neural network, giving robust results with a lower mean - square error at the expense of slow convergence. To overcome this problem, in this work, a fast converging recursive least squares (RLS) - based complex - valued backpropagation learning algorithm is derived for S & G - DD blind equalization. Simulation results show the effectiveness of the proposed algorithm in terms of initial convergence.

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