Data Filtering Based Multi-innovation Gradient Identification Methods for Feedback Nonlinear Systems

With the development of industry information technology, many researchers pay attention to the estimation problems of feedback nonlinear systems increasingly. In this paper, a filtering based multi-innovation stochastic gradient algorithm is derived for Hammerstein equation-error autoregressive systems by using the hierarchical technique. The parameter estimates accuracy can be improved with the innovation length increasing. These algorithms are easy to implement on-line. The simulation results verify the effectiveness of the proposed algorithm.