非均匀Hammerstein-Wiener系统的递阶随机梯度辨识算法  

Hierarchical stochastic gradient identification for Hammerstein-Wiener systems with non-uniformly sampling

作　　者：刘冉冉[1,2] 潘天红[1] 李正明[1] 

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212013 [2]江苏理工学院汽车与交通工程学院,江苏常州213001

出　　处：《控制与决策》2015年第8期1491-1496,共6页Control and Decision

基　　金：国家自然科学基金项目(61273142);江苏省自然科学基金项目(BK2011466);江苏省研究生培养创新工程项目(CXLX12 0648);江苏省六大人才高峰项目(2012-DZXX-045);江苏省高校优势学科建设工程项目(PAPD)

摘　　要：针对一类非均匀数据采样Hammerstein-Wiener系统,提出一种递阶多新息随机梯度算法.首先基于提升技术,推导出系统的状态空间模型,并考虑因果约束关系,将该模型分解成两个子系统,利用多新息遗忘随机梯度算法辨识出此模型的参数;然后,引入可变遗忘因子,提出一种修正函数并在线确定其大小,提高了算法的收敛速度及抗干扰能力.仿真实例验证了所提出算法的有效性和优越性.A hierarchical multi-innovation stochastic gradient identification algorithm is proposed for HammersteinWiener（H-W） nonlinear systems with non-uniformly sampling. The corresponding state space models of H-W are derived by using the lifting technique. Considering the causality constraints, the H-W system is decomposed into two subsystems firstly. Then the model parameters are identified by using the multi-innovation based stochastic gradient algorithm with forgetting factors. In order to improve the convergent rate and the disturbance rejection, a new kind of variable forgetting factor algorithm is also presented. Simulation examples demonstrate that the proposed algorithm has fast convergence speed and is robust to the noise.

关 键 词：参数估计 随机梯度 Hammerstein-Wiener系统 递阶辨识 可变遗忘因子 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]