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

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

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.

Original language	English
Title of host publication	Neural Networks and Pattern Recognition
Editors	O. Omidvar, J. Dayhoff
Place of Publication	Cambridge
Publisher	Academic Press Inc.
Chapter	10
Pages	311-344
Number of pages	34
ISBN (Print)	978-0-12-526420-4
DOIs	https://doi.org/10.1016/B978-012526420-4/50011-2
Publication status	Published - 1 Dec 1998
Externally published	Yes

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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",

year = "1998",

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doi = "10.1016/B978-012526420-4/50011-2",

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AU - Principe, Jose

AU - Celebi, Samel

AU - de Vries, Bert

AU - Harris, John

PY - 1998/12/1

Y1 - 1998/12/1

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

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

M3 - Chapter

SN - 978-0-12-526420-4

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EP - 344

BT - Neural Networks and Pattern Recognition

A2 - Omidvar, O.

A2 - Dayhoff, J.

PB - Academic Press Inc.

CY - Cambridge

ER -

Locally recurrent networks : the gamma operator, properties and extensions

Abstract

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