Parameter identification of a class of nonlinear systems based on the multi-innovation identification theory

In this paper, the parameter identification problems of a class of linear-in-parameters systems are studied. Based on the multi-innovation identification theory, a multi-innovation stochastic gradient algorithm and a filtering based multi-innovation stochastic gradient algorithm are proposed. A nonlinear example is used to verify the effectiveness of the proposed algorithms and the results are compared in terms of estimation accuracy and computational efficiency. The simulation results show that the filtering based multi-innovation stochastic gradient algorithm is capable of producing highly accurate parameter estimates.