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

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.