WLAN indoor GA-ANN positioning algorithm via regularity encoding optimization

Lin Ma; Ying Sun; Mu Zhou; Yubin Xu

doi:10.1109/ICCIIS.2010.25

WLAN indoor GA-ANN positioning algorithm via regularity encoding optimization

Lin Ma^*
, Ying Sun
, Mu Zhou
, Yubin Xu

^*Corresponding author for this work

School of Electronics and Information Engineering, Harbin Institute of Technology

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

4 Scopus citations

Abstract

To begin with, for indoor location system, the necessity of research on genetic neural network and its math model are introduced. Then, by analyzing principle of genetic optimized artificial neural network, an indoor location math model of genetic neural network is established. As for various coding types, regularity is taken as the measurement to determine the best coding type for parameter optimization. By analyzing theory of splicing/decomposable coding, the advantages of regularity for such coding type are proved. Finally, through simulation comparisons, to select a regularity coding type for GA-ANN can improve positioning accuracy for indoor environment effectively.

Original language	English
Title of host publication	Proceedings - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010
Pages	261-265
Number of pages	5
DOIs	https://doi.org/10.1109/ICCIIS.2010.25
State	Published - 2010
Externally published	Yes
Event	2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010 - Nanning, Guangxi, China Duration: 13 Oct 2010 → 14 Oct 2010

Publication series

Name	Proceedings - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010

Conference

Conference	2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010
Country/Territory	China
City	Nanning, Guangxi
Period	13/10/10 → 14/10/10

Keywords

Genetic neural network
Indoor location
Regularity
Splicing/decomposable coding

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10.1109/ICCIIS.2010.25

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Ma, L., Sun, Y., Zhou, M., & Xu, Y. (2010). WLAN indoor GA-ANN positioning algorithm via regularity encoding optimization. In Proceedings - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010 (pp. 261-265). Article 5629237 (Proceedings - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010). https://doi.org/10.1109/ICCIIS.2010.25

@inproceedings{b77529278a6f49329ededad609c726c9,

title = "WLAN indoor GA-ANN positioning algorithm via regularity encoding optimization",

abstract = "To begin with, for indoor location system, the necessity of research on genetic neural network and its math model are introduced. Then, by analyzing principle of genetic optimized artificial neural network, an indoor location math model of genetic neural network is established. As for various coding types, regularity is taken as the measurement to determine the best coding type for parameter optimization. By analyzing theory of splicing/decomposable coding, the advantages of regularity for such coding type are proved. Finally, through simulation comparisons, to select a regularity coding type for GA-ANN can improve positioning accuracy for indoor environment effectively.",

keywords = "Genetic neural network, Indoor location, Regularity, Splicing/decomposable coding",

author = "Lin Ma and Ying Sun and Mu Zhou and Yubin Xu",

year = "2010",

doi = "10.1109/ICCIIS.2010.25",

language = "英语",

isbn = "9780769542607",

series = "Proceedings - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010",

pages = "261--265",

booktitle = "Proceedings - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010",

note = "2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010 ; Conference date: 13-10-2010 Through 14-10-2010",

}

Ma, L, Sun, Y, Zhou, M & Xu, Y 2010, WLAN indoor GA-ANN positioning algorithm via regularity encoding optimization. in Proceedings - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010., 5629237, Proceedings - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010, pp. 261-265, 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010, Nanning, Guangxi, China, 13/10/10. https://doi.org/10.1109/ICCIIS.2010.25

WLAN indoor GA-ANN positioning algorithm via regularity encoding optimization. / Ma, Lin; Sun, Ying; Zhou, Mu et al.
Proceedings - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010. 2010. p. 261-265 5629237 (Proceedings - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010).

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

TY - GEN

T1 - WLAN indoor GA-ANN positioning algorithm via regularity encoding optimization

AU - Ma, Lin

AU - Sun, Ying

AU - Zhou, Mu

AU - Xu, Yubin

PY - 2010

Y1 - 2010

N2 - To begin with, for indoor location system, the necessity of research on genetic neural network and its math model are introduced. Then, by analyzing principle of genetic optimized artificial neural network, an indoor location math model of genetic neural network is established. As for various coding types, regularity is taken as the measurement to determine the best coding type for parameter optimization. By analyzing theory of splicing/decomposable coding, the advantages of regularity for such coding type are proved. Finally, through simulation comparisons, to select a regularity coding type for GA-ANN can improve positioning accuracy for indoor environment effectively.

AB - To begin with, for indoor location system, the necessity of research on genetic neural network and its math model are introduced. Then, by analyzing principle of genetic optimized artificial neural network, an indoor location math model of genetic neural network is established. As for various coding types, regularity is taken as the measurement to determine the best coding type for parameter optimization. By analyzing theory of splicing/decomposable coding, the advantages of regularity for such coding type are proved. Finally, through simulation comparisons, to select a regularity coding type for GA-ANN can improve positioning accuracy for indoor environment effectively.

KW - Genetic neural network

KW - Indoor location

KW - Regularity

KW - Splicing/decomposable coding

UR - https://www.scopus.com/pages/publications/79951893467

U2 - 10.1109/ICCIIS.2010.25

DO - 10.1109/ICCIIS.2010.25

M3 - 会议稿件

AN - SCOPUS:79951893467

SN - 9780769542607

T3 - Proceedings - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010

SP - 261

EP - 265

BT - Proceedings - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010

T2 - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010

Y2 - 13 October 2010 through 14 October 2010

ER -

Ma L, Sun Y, Zhou M, Xu Y. WLAN indoor GA-ANN positioning algorithm via regularity encoding optimization. In Proceedings - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010. 2010. p. 261-265. 5629237. (Proceedings - 2010 International Conference on Communications and Intelligence Information Security, ICCIIS 2010). doi: 10.1109/ICCIIS.2010.25

WLAN indoor GA-ANN positioning algorithm via regularity encoding optimization

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