@inproceedings{9020030995fb44479cd25f088a249272,
title = "Support vector machine based on Genetic Algorithm integrated navigation fault detection parameter optimization method",
abstract = "In order to solve the problem of low fault detection rate of combinatorial navigation due to the mismatch of support vector machine parameters, this paper uses genetic algorithm and lattice search method to find the optimal support vector machine penalty parameter C and kernel function parameter g. The result of the search is brought into the support vector machine to obtain the classification model and finally classify the combinatorial navigation data. The results show that the genetic algorithm has a faster search speed and a higher classification accuracy.",
keywords = "Genetic Algorithm, Integrated navigation system, fault detection, support vector machine",
author = "Huaijian Li and Jing Fang and Xiaojing Du and Ziye Hu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022 ; Conference date: 28-10-2022 Through 30-10-2022",
year = "2022",
doi = "10.1109/DOCS55193.2022.9967741",
language = "English",
series = "2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022",
address = "United States",
}