有理模型辨识的两类新方法—–混合迭代与柔性最小二乘法  

Two novel identification methods for rational models——Compound iterative algorithm and flexible least squares algorithm

作　　者：陈晶[1] 朱全民 CHEN Jing;ZHU Quan-min(School of Science,Jiangnan University,Wuxi 214122,China;Department of Engineering Design and Mathematics,University of the West of England,Bristol BS161QY,U.K.)

机构地区：[1]江南大学理学院,江苏无锡214122 [2]西英格兰大学工业设计和数学系,英国布里斯托BS161QY

出　　处：《控制与决策》2022年第1期58-66,共9页Control and Decision

基　　金：国家自然科学基金项目(61973137);近地面探测技术重点实验室基金项目(TCGZ2019A001);中央高校基本科研业务费专项资金项目(JUSRP22016)。

摘　　要：针对有理模型提出两类辨识方法.首先提出基于递阶辨识思想的混合辨识方法,将模型分解为分子和分母两个子模型,分别用最小二乘法辨识分子参数,用粒子群算法和智能多步长梯度迭代算法辨识分母参数.由于降低了模型维数,且信息向量与噪声不相关,相对于传统的偏差补偿最小二乘算法,混合迭代法可以提高辨识精度并降低计算量.然后,为消除模型结构已知的假设,且充分利用最新数据更新系统参数,提出柔性递推最小二乘辨识方法,将有理模型转化为时变参数系统,进而辨识出时变系统的参数.仿真例子验证了所提出方法的有效性.This study proposes two identification methods for nonlinear rational models.The first is the compound iterative algorithm which is based on the hierarchical technique,which transforms the rational model into two sub-models whose parameters are estimated iteratively using the particle swarm optimization algorithm and intelligent multi-steplength algorithm,respectively.Thus,it has less computational efforts and higher estimation accuracy when compared with the traditional identification methods.Then,a flexible recursive least squares algorithm is proposed which turns the rational model into a time-varying model,thus it does not require the knowledge of the structure of the denominator model,and can update the parameters with new collected data.Simulation results show the effectiveness of the proposed algorithms.

关 键 词：有理模型 参数估计 梯度迭代 粒子群算法 柔性递推最小二乘算法 混合辨识算法 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]