Least-squares-based iterative and gradient-based iterative estimation algorithms for bilinear systems

被引：50
|
作者：
Li, Meihang [1 ]
Liu, Ximei [1 ]
Ding, Feng [1 ,2 ]
机构：
[1] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao 266042, Peoples R China
[2] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia
来源：
NONLINEAR DYNAMICS | 2017年 / 89卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Parameter estimation; Iterative identification; Gradient search; Least squares; Hierarchical identification; Bilinear system; MOVING AVERAGE SYSTEMS; PARAMETER-ESTIMATION; DYNAMIC-SYSTEMS; IDENTIFICATION ALGORITHM; HAMMERSTEIN SYSTEMS; FILTERING TECHNIQUE; NONLINEAR-SYSTEMS; NEWTON ITERATION; AUXILIARY MODEL; OPTIMIZATION;
D O I：
10.1007/s11071-017-3445-x
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
For bilinear systems with colored noise, this paper gives the input-output representation of the bilinear systems through eliminating the state variables in the model and derives a three-stage gradient-based iterative algorithm and a three-stage least-squares-based iterative algorithm for identifying the parameters of the input-output representation by means of the hierarchical identification principle. A gradient-based iterative (GI) algorithm is given for comparison. Compared with the GI algorithm, the proposed algorithms have lower computational burden and faster convergence speed. The simulation results indicate that the proposed algorithms are more effective for identifying bilinear systems.
引用
收藏
页码：197 / 211
页数：15
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