Locally recurrent networks : the gamma operator, properties and extensions

Jose Principe; Samel Celebi; Bert de Vries; John Harris

doi:10.1016/B978-012526420-4/50011-2

Locally recurrent networks : the gamma operator, properties and extensions

Jose Principe
, Samel Celebi
, Bert de Vries
, John Harris

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Hoofdstuk › Academic › peer review

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Locally recurrent networks have shown great potential for processing time- varying signals. This paper reviews various memory structures for time- varying signal processing with neural networks. In particular, we focus on the gamma structure and variations such as the Laguerre and gamma II memory networks. The paper presents the basic theory of memory structures and several interpretations of their function.

Originele taal-2	Engels
Titel	Neural Networks and Pattern Recognition
Redacteuren	O. Omidvar, J. Dayhoff
Plaats van productie	Cambridge
Uitgeverij	Academic Press Inc.
Hoofdstuk	10
Pagina's	311-344
Aantal pagina's	34
ISBN van geprinte versie	978-0-12-526420-4
DOI's	https://doi.org/10.1016/B978-012526420-4/50011-2
Status	Gepubliceerd - 1 dec. 1998
Extern gepubliceerd	Ja

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10.1016/B978-012526420-4/50011-2

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title = "Locally recurrent networks : the gamma operator, properties and extensions",

abstract = "Locally recurrent networks have shown great potential for processing time- varying signals. This paper reviews various memory structures for time- varying signal processing with neural networks. In particular, we focus on the gamma structure and variations such as the Laguerre and gamma II memory networks. The paper presents the basic theory of memory structures and several interpretations of their function.",

author = "Jose Principe and Samel Celebi and \{de Vries\}, Bert and John Harris",

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AU - Principe, Jose

AU - Celebi, Samel

AU - de Vries, Bert

AU - Harris, John

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N2 - Locally recurrent networks have shown great potential for processing time- varying signals. This paper reviews various memory structures for time- varying signal processing with neural networks. In particular, we focus on the gamma structure and variations such as the Laguerre and gamma II memory networks. The paper presents the basic theory of memory structures and several interpretations of their function.

AB - Locally recurrent networks have shown great potential for processing time- varying signals. This paper reviews various memory structures for time- varying signal processing with neural networks. In particular, we focus on the gamma structure and variations such as the Laguerre and gamma II memory networks. The paper presents the basic theory of memory structures and several interpretations of their function.

U2 - 10.1016/B978-012526420-4/50011-2

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PB - Academic Press Inc.

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Locally recurrent networks : the gamma operator, properties and extensions

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