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The past, present, and future of neural networks for signal processing

Jenq-Nen Hwang ; Sun-Yan Kung ; Niranjan, M. ; Principe, J.C.

IEEE signal processing magazine, 1997-11, Vol.14 (6), p.28-48

New York, NY: IEEE

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Título:
The past, present, and future of neural networks for signal processing
Autor: Jenq-Nen Hwang ; Sun-Yan Kung ; Niranjan, M. ; Principe, J.C.
Assuntos: Applied sciences ; Artificial intelligence ; Biological and medical sciences ; Biomedical optical imaging ; Cardiovascular system ; Character recognition ; Computer science; control theory; systems ; Connectionism. Neural networks ; Exact sciences and technology ; Image processing ; Information, signal and communications theory ; Investigative techniques, diagnostic techniques (general aspects) ; Medical sciences ; Network synthesis ; Neural networks ; Optical character recognition software ; Optical computing ; Optical signal processing ; Signal processing ; Speech recognition ; Statistical learning ; Telecommunications and information theory ; Ultrasonic investigative techniques ; Vehicle dynamics
É parte de: IEEE signal processing magazine, 1997-11, Vol.14 (6), p.28-48
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
Descrição: The article provides a review of the fundamental of neural networks and reports recent progress. Topics covered include dynamic modeling, model-based neural networks, statistical learning, eigenstructure-based processing, active learning, and generalization capability. Current and potential applications of neural networks are also described in detail. Those applications include optical character recognition, speech recognition and synthesis, automobile and aircraft control, image analysis and neural vision, and several medical applications. Essentially, neural networks have become a very effective tool in signal processing, particularly in various recognition tasks.
Editor: New York, NY: IEEE
Idioma: Inglês

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The past, present, and future of neural networks for signal processing

Jenq-Nen Hwang ; Sun-Yan Kung ; Niranjan, M. ; Principe, J.C.

New York, NY: IEEE

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