Weighted Parameter Estimation for Hammerstein Nonlinear ARX Systems

This paper proposes parameter estimation algorithms for Hammerstein nonlinear ARX systems. By making full use of the current and previous input–output data of the system, a weighted multi-innovation stochastic gradient algorithm is presented to improve the convergence rate of identification. The innovation term in the traditional identification algorithms can be treated as a particle in the particle-filtering technique, and the weight of each innovation then can be computed according to their importance. The simulation results indicate that the algorithm can improve the accuracy of parameter estimation.