Decomposition-based over-parameterization forgetting factor stochastic gradient algorithm for Hammerstein-Wiener nonlinear systems with non-uniform sampling

This article investigates the parameter estimation problems of Hammerstein-Wiener nonlinear systems with non-uniform sampling. The over-parameterization identification model for the Hammerstein-Wiener nonlinear systems is established from the non-uniformly sampled input-output data. By applying the gradient search principle, we derive an over-parameterization forgetting factor stochastic gradient algorithm for identifying the nonlinear systems. In order to improve the parameter estimation accuracy, a decomposition-based over-parameterization forgetting factor stochastic gradient algorithm is presented by using the decomposition technique. The key is to transform the original system into two subsystems and to estimate the parameters of each subsystem, respectively. The simulation results indicate that the proposed algorithms are effective.