IEICE TRANSACTIONS on Communications
Neural Network Location Based on Weight Optimization with Genetic Algorithm under the Condition of Less Information
Summary :
This paper puts forward the concept of cellular network location with less information which can overcome the weaknesses of the cellular location technology in practical applications. After a systematic introduction of less-information location model, this paper presents a location algorithm based on AGA (Adaptive Genetic Algorithm) and an optimized RBF (Radical Basis Function) neural network. The virtues of this algorithm are that it has high location accuracy, reduces the location measurement parameters and effectively enhances the robustness. The simulation results show that under the condition of less information, the optimized location algorithm can effectively solve the fuzzy points in the location model and satisfy the FCC's (Federal Communications Commission) requirements on location accuracy.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E99-B No.11 pp.2323-2331
- Publication Date
- 2016/11/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2015EBP3284
- Type of Manuscript
- PAPER
- Category
- Fundamental Theories for Communications
Authors
Jian Hui WANG
Zhengzhou Institute of Information Science and Technology
Jia Liang WANG
China National Software and Service Co., Ltd
Da Ming WANG
Zhengzhou Institute of Information Science and Technology
Wei Jia CUI
Zhengzhou Institute of Information Science and Technology
Xiu Kun REN
Zhengzhou Institute of Information Science and Technology
Keyword
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Jian Hui WANG, Jia Liang WANG, Da Ming WANG, Wei Jia CUI, Xiu Kun REN, "Neural Network Location Based on Weight Optimization with Genetic Algorithm under the Condition of Less Information" in IEICE TRANSACTIONS on Communications,
vol. E99-B, no. 11, pp. 2323-2331, November 2016, doi: 10.1587/transcom.2015EBP3284.
Abstract: This paper puts forward the concept of cellular network location with less information which can overcome the weaknesses of the cellular location technology in practical applications. After a systematic introduction of less-information location model, this paper presents a location algorithm based on AGA (Adaptive Genetic Algorithm) and an optimized RBF (Radical Basis Function) neural network. The virtues of this algorithm are that it has high location accuracy, reduces the location measurement parameters and effectively enhances the robustness. The simulation results show that under the condition of less information, the optimized location algorithm can effectively solve the fuzzy points in the location model and satisfy the FCC's (Federal Communications Commission) requirements on location accuracy.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2015EBP3284/_p
Copy
@ARTICLE{e99-b_11_2323,
author={Jian Hui WANG, Jia Liang WANG, Da Ming WANG, Wei Jia CUI, Xiu Kun REN, },
journal={IEICE TRANSACTIONS on Communications},
title={Neural Network Location Based on Weight Optimization with Genetic Algorithm under the Condition of Less Information},
year={2016},
volume={E99-B},
number={11},
pages={2323-2331},
abstract={This paper puts forward the concept of cellular network location with less information which can overcome the weaknesses of the cellular location technology in practical applications. After a systematic introduction of less-information location model, this paper presents a location algorithm based on AGA (Adaptive Genetic Algorithm) and an optimized RBF (Radical Basis Function) neural network. The virtues of this algorithm are that it has high location accuracy, reduces the location measurement parameters and effectively enhances the robustness. The simulation results show that under the condition of less information, the optimized location algorithm can effectively solve the fuzzy points in the location model and satisfy the FCC's (Federal Communications Commission) requirements on location accuracy.},
keywords={},
doi={10.1587/transcom.2015EBP3284},
ISSN={1745-1345},
month={November},}
Copy
TY - JOUR
TI - Neural Network Location Based on Weight Optimization with Genetic Algorithm under the Condition of Less Information
T2 - IEICE TRANSACTIONS on Communications
SP - 2323
EP - 2331
AU - Jian Hui WANG
AU - Jia Liang WANG
AU - Da Ming WANG
AU - Wei Jia CUI
AU - Xiu Kun REN
PY - 2016
DO - 10.1587/transcom.2015EBP3284
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E99-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2016
AB - This paper puts forward the concept of cellular network location with less information which can overcome the weaknesses of the cellular location technology in practical applications. After a systematic introduction of less-information location model, this paper presents a location algorithm based on AGA (Adaptive Genetic Algorithm) and an optimized RBF (Radical Basis Function) neural network. The virtues of this algorithm are that it has high location accuracy, reduces the location measurement parameters and effectively enhances the robustness. The simulation results show that under the condition of less information, the optimized location algorithm can effectively solve the fuzzy points in the location model and satisfy the FCC's (Federal Communications Commission) requirements on location accuracy.
ER -
English
