Printed dipole antenna design using artificial neural network modeling for RFID application

Xiuping Li; Jianjun Gao; Georg Boeck

doi:10.1002/mmce.20183

Printed dipole antenna design using artificial neural network modeling for RFID application

Xiuping Li^*
, Jianjun Gao
, Georg Boeck

^*此作品的通讯作者

Beijing University of Posts and Telecommunications
Southeast University, Nanjing
Technical University of Berlin

科研成果: 期刊稿件 › 文章 › 同行评审

12 引用（Scopus）

摘要

An approach to printed dipole antenna design using the artificial neural network (ANN) modeling technique is presented in this article. Three important antennalayout dimensions are used to capture critical input/output relationships in the ANN model. Once fully developed, the ANN model has been shown to be as accurate as an EM simulator and much more efficient computationally in antenna design optimization.

源语言	英语
页（从-至）	607-611
页数	5
期刊	International Journal of RF and Microwave Computer-Aided Engineering
卷	16
期	6
DOI	https://doi.org/10.1002/mmce.20183
出版状态	已出版 - 11月 2006
已对外发布	是

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title = "Printed dipole antenna design using artificial neural network modeling for RFID application",

abstract = "An approach to printed dipole antenna design using the artificial neural network (ANN) modeling technique is presented in this article. Three important antennalayout dimensions are used to capture critical input/output relationships in the ANN model. Once fully developed, the ANN model has been shown to be as accurate as an EM simulator and much more efficient computationally in antenna design optimization.",

keywords = "Artificial neural network, Printed dipole antenna",

author = "Xiuping Li and Jianjun Gao and Georg Boeck",

year = "2006",

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T1 - Printed dipole antenna design using artificial neural network modeling for RFID application

AU - Li, Xiuping

AU - Gao, Jianjun

AU - Boeck, Georg

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N2 - An approach to printed dipole antenna design using the artificial neural network (ANN) modeling technique is presented in this article. Three important antennalayout dimensions are used to capture critical input/output relationships in the ANN model. Once fully developed, the ANN model has been shown to be as accurate as an EM simulator and much more efficient computationally in antenna design optimization.

AB - An approach to printed dipole antenna design using the artificial neural network (ANN) modeling technique is presented in this article. Three important antennalayout dimensions are used to capture critical input/output relationships in the ANN model. Once fully developed, the ANN model has been shown to be as accurate as an EM simulator and much more efficient computationally in antenna design optimization.

KW - Artificial neural network

KW - Printed dipole antenna

UR - https://www.scopus.com/pages/publications/33750868190

U2 - 10.1002/mmce.20183

DO - 10.1002/mmce.20183

M3 - 文章

AN - SCOPUS:33750868190

SN - 1096-4290

VL - 16

SP - 607

EP - 611

JO - International Journal of RF and Microwave Computer-Aided Engineering

JF - International Journal of RF and Microwave Computer-Aided Engineering

IS - 6

ER -

Printed dipole antenna design using artificial neural network modeling for RFID application

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