NDLI: Neural networks expand SP's horizons

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Neural networks expand SP's horizons

Content Provider	IEEE Xplore Digital Library
Author	Haykin, S.
Copyright Year	1991
Abstract	Advanced algorithms for signal processing simultaneously account for nonlinearity, nonstationarity, and non-Gaussianity. The article examines the use of neural networks as an engineering tool for signal processing applications. The aim is three fold: to articulate a new philosophy in the approach to statistical signal processing using neural networks, which (either by themselves or in combination with other suitable techniques) account for the practical realities of nonlinearity, nonstationarity, and non-Gaussianity; to describe three case studies using real-life data, which clearly demonstrate the superiority of this new approach over the classical approaches to statistical signal processing; and to discuss mutual information as a criterion for designing unsupervised neural networks, thus moving away from the mean-square error criterion.
Starting Page	24
Ending Page	49
Page Count	26
File Size	9435839
File Format	PDF
ISSN	10535888
Volume Number	13
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Biological neural networks Signal processing Signal processing algorithms Artificial neural networks Neurons Signal design Humans Multi-layer neural network Fault tolerance
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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