现代防御技术2017，Vol.45Issue(2)：172-176,216,6.DOI:10.3969/j.issn.1009-086x.2017.02.027
ACO-ELM与CFSFDP结合的机载动力系统参数估计
Parameter Estimation of Airborne Power System Based on ACO-ELM and CFSFDP
摘要
Abstract
For the uncertainty of the test data of the airborne power system and the problem of solving parameters with poor real-time performance,a parameter estimation method of the airborne power system based on clustering by fast search and find of density peaks (CFSFDP) and ant colony optimization extreme learning machine (ACO-ELM) is proposed.Firstly,the CFSFDP method is utilized to cluster the test bench data in the whole behavior range,and then,a sub-estimator is designed in each cluster using ACO-ELM.In the process of designing the sub-estimator with ACO-ELM,the particle swarm optimization algorithm is utilized to search the best hidden node number of extreme learning machine for getting the best topological structure.Finally,the training and testing results show that the maximum mean relative error is better than the RBF network,which meets the demand of thrust control and onboard real time state assessment.The method can be used for estimating other immeasurable parameters.关键词
飞行器/推力/参数估计/蚁群/快速寻找密度极点聚类/蚁群极限学习机Key words
vehicle/thrust/parameter estimation/ant colony/clustering by fast search and find of density peaks (CFSFDP)/ant colony optimization extreme learning machine(ACO-ELM)分类
航空航天引用本文复制引用
孟蕾,许爱强,牛景华..ACO-ELM与CFSFDP结合的机载动力系统参数估计[J].现代防御技术,2017,45(2):172-176,216,6.基金项目
“十二五”国防技术基础科研项目(Z052013B004) （Z052013B004）
