Gradient‐based recursive parameter estimation for a periodically nonuniformly sampled‐data Hammerstein–Wiener system based on the key‐term separation
Qilin Liu, Feng Ding
Abstract
Summary The identification of the Hammerstein–Wiener (H‐W) systems based on the nonuniform input–output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled‐data H‐W system. In addition, the product terms of the parameters in the H‐W system are inevitable. In order to solve the problem, the key‐term separation is applied and two algorithms are proposed. One is the key‐term‐based forgetting factor stochastic gradient (KT‐FFSG) algorithm based on the gradient search. The other is the key‐term‐based hierarchical forgetting factor stochastic gradient (KT‐HFFSG) algorithm. Compared with the KT‐FFSG algorithm, the KT‐HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective.