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Subspace learning of neural networks.


Subspace learning of neural networks.

Lv, Jian Cheng et al.

CRC Press


233 pages



Automation and control engineering


Lv et al. (Sichuan U., China) focus on the convergence analysis of subspace learning algorithms of neural networks and the ways to extend the use of these networks in fields like biomedical signal processing, biomedical image processing, and surface fitting. Using the discrete determination time (DDT) method, they consider invariant sets and global boundedness of some algorithms, their convergence conditions, the relationship between a stochastic discrete time algorithm and the corresponding DDT algorithm using block algorithms, and the chaotic and robust properties of algorithms. The book is meant for postgraduates, engineers, researchers, and those working in data mining, image processing, and signal processing.

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Publication:SciTech Book News
Article Type:Book review
Date:Dec 1, 2010
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