A Cross-Associative Neural Network for SVD of Nonsquared Data Marix in Signal Processing  

Oleh:

Feng, Da-Zheng ; Bao, Zheng ; Zhang, Xian-Da

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 5 (2001), page 1215-1221.
Topik: matrix; cross - associative; neural network; SVD; non squared; data matrix; signal processing

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.5 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This paper proposes a cross - associative neural network (CANN) for singular value decomposition (SVD) of a non - squared data matrix in signal processing, in order to improve the convergence speed and avoid the potential instability of the deterministic networks associated with the cross-correlation neural - network models. We study the global asymptotic stability of the network for tracking all the singular components, and show that the selection of its learning rate in the iterative algorithm is independent of the singular value distribution of a non - squared matrix. The performances of CANN are shown via simulations.

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