### 摘要

The geometric dilution of precision is widely used as criterion for selecting the best set of the measurement devices. Some methods had been proposed to rmd the value of the Geometric Dilution of Precision (GDOP), such as using inverse matrix to solve the linear equation. But it takes a large amount of computing time to find the optimal solution. In this paper, we propose to adopt a neural network with gradient descent adaptive learning rate training algorithm to approximate the value of GDOP. The proposed two types of input/output mapping used the element in the matrix as the input data of the neural network. After finishing the training process, it can get a better approximation results with the proposed two types. The simulation result shows that proposed algorithm has lower GDOP approximation error using less Epochs than the other types. For cellular wireless communication system, it will significantly reduce the computational complexity and the training time by selecting the service base station at first with three other base stations to estimate the position of mobile station.

原文 | English |
---|---|

主出版物標題 | Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018 |

發行者 | IEEE Computer Society |

頁面 | 474-479 |

頁數 | 6 |

ISBN（電子） | 9781538652121 |

DOIs | |

出版狀態 | Published - 2018 十一月 7 |

事件 | 17th International Conference on Machine Learning and Cybernetics, ICMLC 2018 - Chengdu, China 持續時間: 2018 七月 15 → 2018 七月 18 |

### 出版系列

名字 | Proceedings - International Conference on Machine Learning and Cybernetics |
---|---|

卷 | 2 |

ISSN（列印） | 2160-133X |

ISSN（電子） | 2160-1348 |

### Other

Other | 17th International Conference on Machine Learning and Cybernetics, ICMLC 2018 |
---|---|

國家 | China |

城市 | Chengdu |

期間 | 18-07-15 → 18-07-18 |

### 指紋

### All Science Journal Classification (ASJC) codes

- Artificial Intelligence
- Computational Theory and Mathematics
- Computer Networks and Communications
- Human-Computer Interaction

### 引用此文

*Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018*(頁 474-479). [8526984] (Proceedings - International Conference on Machine Learning and Cybernetics; 卷 2). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2018.8526984

}

*Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018.*, 8526984, Proceedings - International Conference on Machine Learning and Cybernetics, 卷 2, IEEE Computer Society, 頁 474-479, 17th International Conference on Machine Learning and Cybernetics, ICMLC 2018, Chengdu, China, 18-07-15. https://doi.org/10.1109/ICMLC.2018.8526984

**Applying Geometric Dilution of Precision Approximation to Adaptive Neural Network Learning for Precise Mobile Station Positioning.** / Chen, Chien Sheng; Huan, Jen-Fa; Lin, Siou Cyuan; Tseng, Ching Chuan; Wu, Chia Ming.

研究成果: Conference contribution

TY - GEN

T1 - Applying Geometric Dilution of Precision Approximation to Adaptive Neural Network Learning for Precise Mobile Station Positioning

AU - Chen, Chien Sheng

AU - Huan, Jen-Fa

AU - Lin, Siou Cyuan

AU - Tseng, Ching Chuan

AU - Wu, Chia Ming

PY - 2018/11/7

Y1 - 2018/11/7

N2 - The geometric dilution of precision is widely used as criterion for selecting the best set of the measurement devices. Some methods had been proposed to rmd the value of the Geometric Dilution of Precision (GDOP), such as using inverse matrix to solve the linear equation. But it takes a large amount of computing time to find the optimal solution. In this paper, we propose to adopt a neural network with gradient descent adaptive learning rate training algorithm to approximate the value of GDOP. The proposed two types of input/output mapping used the element in the matrix as the input data of the neural network. After finishing the training process, it can get a better approximation results with the proposed two types. The simulation result shows that proposed algorithm has lower GDOP approximation error using less Epochs than the other types. For cellular wireless communication system, it will significantly reduce the computational complexity and the training time by selecting the service base station at first with three other base stations to estimate the position of mobile station.

AB - The geometric dilution of precision is widely used as criterion for selecting the best set of the measurement devices. Some methods had been proposed to rmd the value of the Geometric Dilution of Precision (GDOP), such as using inverse matrix to solve the linear equation. But it takes a large amount of computing time to find the optimal solution. In this paper, we propose to adopt a neural network with gradient descent adaptive learning rate training algorithm to approximate the value of GDOP. The proposed two types of input/output mapping used the element in the matrix as the input data of the neural network. After finishing the training process, it can get a better approximation results with the proposed two types. The simulation result shows that proposed algorithm has lower GDOP approximation error using less Epochs than the other types. For cellular wireless communication system, it will significantly reduce the computational complexity and the training time by selecting the service base station at first with three other base stations to estimate the position of mobile station.

UR - http://www.scopus.com/inward/record.url?scp=85058042955&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058042955&partnerID=8YFLogxK

U2 - 10.1109/ICMLC.2018.8526984

DO - 10.1109/ICMLC.2018.8526984

M3 - Conference contribution

AN - SCOPUS:85058042955

T3 - Proceedings - International Conference on Machine Learning and Cybernetics

SP - 474

EP - 479

BT - Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018

PB - IEEE Computer Society

ER -