Neural network communication equalizer with optimized solution capability

David C. Chen; Bing J. Sheu; Eric Y. Chou

Neural network communication equalizer with optimized solution capability

David C. Chen^*, Bing J. Sheu, Eric Y. Chou

^*Corresponding author for this work

University of Southern California

Research output: Contribution to conference › Conference Paper › peer-review

1 Scopus citations

Abstract

Artificial neural network approaches in communication have been motivated by the adaptive learning capability and the collective computational properties to process real world signals. In this paper, an one-dimensional Compact Neural Network receiver as a paralleled computational framework of the maximum likelihood sequence estimation (MLSE) is presented. Optimum solution can be obtained by applying the hardware annealing which is a deterministic method for searching a globally minimum energy state in a short period of time.

Original language	English
Pages	1957-1962
Number of pages	6
State	Published - 1996
Externally published	Yes
Event	Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA Duration: 03 06 1996 → 06 06 1996

Conference

Conference	Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)
City	Washington, DC, USA
Period	03/06/96 → 06/06/96

Cite this

@conference{e4c1f8d1cdcb489cbcf8cc749788d13e,

title = "Neural network communication equalizer with optimized solution capability",

abstract = "Artificial neural network approaches in communication have been motivated by the adaptive learning capability and the collective computational properties to process real world signals. In this paper, an one-dimensional Compact Neural Network receiver as a paralleled computational framework of the maximum likelihood sequence estimation (MLSE) is presented. Optimum solution can be obtained by applying the hardware annealing which is a deterministic method for searching a globally minimum energy state in a short period of time.",

author = "Chen, {David C.} and Sheu, {Bing J.} and Chou, {Eric Y.}",

year = "1996",

language = "英语",

pages = "1957--1962",

note = "Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) ; Conference date: 03-06-1996 Through 06-06-1996",

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T1 - Neural network communication equalizer with optimized solution capability

AU - Chen, David C.

AU - Sheu, Bing J.

AU - Chou, Eric Y.

PY - 1996

Y1 - 1996

N2 - Artificial neural network approaches in communication have been motivated by the adaptive learning capability and the collective computational properties to process real world signals. In this paper, an one-dimensional Compact Neural Network receiver as a paralleled computational framework of the maximum likelihood sequence estimation (MLSE) is presented. Optimum solution can be obtained by applying the hardware annealing which is a deterministic method for searching a globally minimum energy state in a short period of time.

AB - Artificial neural network approaches in communication have been motivated by the adaptive learning capability and the collective computational properties to process real world signals. In this paper, an one-dimensional Compact Neural Network receiver as a paralleled computational framework of the maximum likelihood sequence estimation (MLSE) is presented. Optimum solution can be obtained by applying the hardware annealing which is a deterministic method for searching a globally minimum energy state in a short period of time.

UR - http://www.scopus.com/inward/record.url?scp=0029748010&partnerID=8YFLogxK

M3 - 论文

AN - SCOPUS:0029748010

SP - 1957

EP - 1962

T2 - Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)

Y2 - 3 June 1996 through 6 June 1996

ER -

Neural network communication equalizer with optimized solution capability

Abstract

Conference

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