Hierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principle

作者：Chen, Huibo; Xiao, Yongsong; Ding, Feng^*

来源：Applied Mathematics and Computation, 2014, 247: 1202-1210.

DOI：10.1016/j.amc.2014.09.070

• Stochastic gradient
• Parameter estimation
• Hierarchical identification
• Auxiliary model
• Hammerstein system

In this paper, we use the hierarchical identification principle to decompose a Hammerstein controlled autoregressive system into three subsystems, apply the key term separation principle to express the system output as a linear combination of the system parameters, and then derive a hierarchical gradient parameter estimation algorithm for identifying all subsystems. Finally, a multi-innovation stochastic gradient algorithm is presented to improve the estimation accuracy by making full of the identification innovation. The simulation results show that the proposed algorithm is effective.

• 出版日期2014-11-15
• 单位江南大学