摘要
提出了一种基K-均值聚类和约简最小二乘支持向量回归机的推力估计器设计方法.首先用K-均值聚类法将全包线范围内的数据进行聚类,然后在每一个类当中,用迭代约简最小二乘支持向量回归机设计一个子推力估计器.在用迭代约简最小二乘支持向量回归机设计子推力估计器的过程中,为了使计算数值更稳定,用Cholesky分解代替原来的迭代方法.最后仿真实验表明,此推力估计器能满足直接推力控制的需要,并和其它的方案比较起来,该方案存在一定的优势.
In this paper,a design scheme for thrust estimator was proposed based on K-means clustering and reduced least squares support vector regression. Firstly,the K-means clustering algorithm was utilized to cluster the data in the full flight envelope,and the,a sub-estimator was designed in each cluster using the recursive reduced least squares support vector regression （RRLSSVR）. In the process of designing the sub-estimator with RRLSSVR,the Cholesky factorization was utilized to replace the original iteration method for achieving higher numerical stability. Finally,the simulation experiments show that the thrust estimator can satisfy the requirement of direct thrust control for aeroengines,and as compared with other scheme for thrust estimator,the proposed scheme in this paper is also superior.
出处
《航空动力学报》
EI
CAS
CSCD
北大核心
2010年第5期1177-1183,共7页
Journal of Aerospace Power
基金
国家自然科学基金(50576033)
关键词
支持向量机
最小二乘
K-均值聚类
直接推力控制
support vector machine
least squares
K-means clustering
direct thrust control
