Auxiliary Model Based Multi-Innovation Stochastic Gradient Identification Algorithm for Periodically Non-Uniformly Sampled-Data Hammerstein Systems

• Main Authors: Li Xie, Huizhong Yang
• Format: Article
• Language: English
• Published: MDPI AG 2017-07-01
• Series: Algorithms
• Subjects: non-uniform sampling; Hammerstein system; parameter estimation; multi-innovation theory; stochastic gradient algorithm
• Online Access: https://www.mdpi.com/1999-4893/10/3/84

Due to the lack of powerful model description methods, the identification of Hammerstein systems based on the non-uniform input-output dataset remains a challenging problem. This paper introduces a time-varying backward shift operator to describe periodically non-uniformly sampled-data Hammerstein systems, which can simplify the structure of the lifted models using the traditional lifting technique. Furthermore, an auxiliary model-based multi-innovation stochastic gradient algorithm is presented to estimate the parameters involved in the linear and nonlinear blocks. The simulation results confirm that the proposed algorithm is effective and can achieve a high estimation performance.