Support vector machine based on Genetic Algorithm integrated navigation fault detection parameter optimization method

Huaijian Li; Jing Fang; Xiaojing Du; Ziye Hu

doi:10.1109/DOCS55193.2022.9967741

Support vector machine based on Genetic Algorithm integrated navigation fault detection parameter optimization method

Huaijian Li, Jing Fang, Xiaojing Du^*, Ziye Hu

^*Corresponding author for this work

School of Aerospace Engineering

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

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.

Original language	English
Title of host publication	2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665459822
DOIs	https://doi.org/10.1109/DOCS55193.2022.9967741
Publication status	Published - 2022
Event	4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022 - Chengdu, China Duration: 28 Oct 2022 → 30 Oct 2022

Publication series

Name	2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

Conference

Conference	4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022
Country/Territory	China
City	Chengdu
Period	28/10/22 → 30/10/22

Keywords

Genetic Algorithm
Integrated navigation system
fault detection
support vector machine

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10.1109/DOCS55193.2022.9967741

Cite this

Li, H., Fang, J., Du, X., & Hu, Z. (2022). Support vector machine based on Genetic Algorithm integrated navigation fault detection parameter optimization method. In 2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022 (2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DOCS55193.2022.9967741

Li, Huaijian ; Fang, Jing ; Du, Xiaojing et al. / Support vector machine based on Genetic Algorithm integrated navigation fault detection parameter optimization method. 2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022).

@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",

}

Li, H, Fang, J, Du, X & Hu, Z 2022, Support vector machine based on Genetic Algorithm integrated navigation fault detection parameter optimization method. in 2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022. 2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022, Institute of Electrical and Electronics Engineers Inc., 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022, Chengdu, China, 28/10/22. https://doi.org/10.1109/DOCS55193.2022.9967741

Support vector machine based on Genetic Algorithm integrated navigation fault detection parameter optimization method. / Li, Huaijian; Fang, Jing; Du, Xiaojing et al.
2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022).

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

TY - GEN

T1 - Support vector machine based on Genetic Algorithm integrated navigation fault detection parameter optimization method

AU - Li, Huaijian

AU - Fang, Jing

AU - Du, Xiaojing

AU - Hu, Ziye

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Genetic Algorithm

KW - Integrated navigation system

KW - fault detection

KW - support vector machine

UR - http://www.scopus.com/inward/record.url?scp=85144635177&partnerID=8YFLogxK

U2 - 10.1109/DOCS55193.2022.9967741

DO - 10.1109/DOCS55193.2022.9967741

M3 - Conference contribution

AN - SCOPUS:85144635177

T3 - 2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

BT - 2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

Y2 - 28 October 2022 through 30 October 2022

ER -

Li H, Fang J, Du X, Hu Z. Support vector machine based on Genetic Algorithm integrated navigation fault detection parameter optimization method. In 2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022). doi: 10.1109/DOCS55193.2022.9967741

Support vector machine based on Genetic Algorithm integrated navigation fault detection parameter optimization method

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