Auxiliary model based multi-innovation stochastic gradient identification algorithm for periodically non-uniformly sampled-data Hammerstein systems

作者：Xie Li^*; Yang Huizhong

来源：Algorithms, 2017, 10(3): 84.

DOI：10.3390/a10030084

• Estimation performance
• Hammerstein system
• Identification algorithms
• Innovation theory
• Lifting techniques
• Nonuniform sampling
• Stochastic gradient
• Stochastic gradient algorithms

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.

• 出版日期2017
• 单位江南大学