Parameter identification of Volterra nonlinear system based on Levenberg-Marquardt recursive algorithm

The Volterra model can approximate many nonlinear systems, and it is a typical nonlinear system. This paper studies the parameter estimation problem of the Volterra model. Combining the Levenberg-Marquardt optimization method and the recursive identification method, we propose a Levenberg-Marquardt recursive algorithm and apply it to the identification of the Volterra system. In order to verify the feasibility of the above algorithm, the second-order Volterra system is simulated using the Levenberg-Marquardt recursive algorithm and the forgetting factor stochastic gradient algorithm respectively, and then we compare the simulation results of the Volterra system under the two algorithms. The simulation results show that the above two algorithms can identify the parameters of the Volterra system. Compared with the forgetting factor stochastic gradient algorithm, the Levenberg-Marquardt recursive algorithm has faster convergence speed and higher convergence accuracy. This proves the effectiveness of the Levenberg-Marquardt recursive algorithm.