Application of Nonlinear Classification Algorithm in Communication Interference Evaluation

Yifan Chen; Zheng Dou; Hui Han; Xianglong Zhou; Yun Lin

Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings

Research Article

Application of Nonlinear Classification Algorithm in Communication Interference Evaluation

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@INPROCEEDINGS{10.1007/978-3-030-19086-6_49,
    author={Yifan Chen and Zheng Dou and Hui Han and Xianglong Zhou and Yun Lin},
    title={Application of Nonlinear Classification Algorithm in Communication Interference Evaluation},
    proceedings={Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings},
    proceedings_a={ADHIP},
    year={2019},
    month={5},
    keywords={Interference level assessment BP neural network Support vector machine},
    doi={10.1007/978-3-030-19086-6_49}
}

Yifan Chen
Zheng Dou
Hui Han
Xianglong Zhou
Yun Lin
Year: 2019
Application of Nonlinear Classification Algorithm in Communication Interference Evaluation
ADHIP
Springer
DOI: 10.1007/978-3-030-19086-6_49

Yifan Chen¹, Zheng Dou¹, Hui Han², Xianglong Zhou¹, Yun Lin¹^,*

1: Harbin Engineering University
2: State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE)

*Contact email: linyun_phd@hrbeu.edu.cn

Abstract

Traditional methods of communication interference assessment belong third-party assessments that fail to meet the needs of real-time assessments. This paper proposes an interference level evaluation method under the nonlinear classification algorithm. Firstly, building data set with the eigenvalues that affect the interference effect, and then simulation verify by BP neural network and support vector machine. The simulation results verify the feasibility in communication interference assessment and providing the possibility for real-time evaluation.

Keywords: Interference level assessment BP neural network Support vector machine

Published: 2019-05-13
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-030-19086-6_49