Airborne Electronic Equipment Health Condition Assessment Technique

DOI

10.2991/ame-16.2016.226How to use a DOI?

Keywords

Airborne Electronic Equipment, Parameter Estimate, Clustering by Fast Search and Find of Density Peaks, Particle Swarm Optimization Extreme Learning Machine.

Abstract

Airborne Electronic Equipment data is unmeasurable, and computing the parameter based on component model is inaccurate and is time costing. The paper presents airborne electronic equipment parameter estimate method based on clustering by fast search and find of density peaks (CFSFDP) and ant colony optimization extreme learning machine (ACO-ELM). Firstly, the CFSFDP method was utilized to cluster the test bench data in the whole behavior range, and then, a sub-estimator was designed in each cluster using ACO-ELM. In the process of designing the sub-estimator with ACO-ELM, the particle swarm optimization algorithm was utilized to search the best hidden nodes number of extreme learning machine for getting the best topological structure.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Ai-Qiang Xu
AU  - Lei Meng
AU  - Jing-Hua Zhu
PY  - 2016/06
DA  - 2016/06
TI  - Airborne Electronic Equipment Health Condition Assessment Technique
BT  - Proceedings of the 2nd Annual International Conference on Advanced Material Engineering (AME 2016)
PB  - Atlantis Press
SP  - 1383
EP  - 1389
SN  - 2352-5401
UR  - https://doi.org/10.2991/ame-16.2016.226
DO  - 10.2991/ame-16.2016.226
ID  - Xu2016/06
ER  -