Identification methods for Wiener nonlinear systems based on the least squares and gradient iterations

This paper derives a least squares based and a gradient based iterative identification algorithms for Wiener nonlinear systems. These methods separate one bilinear-parameter cost function into two linear-parameter cost functions, estimating directly the parameters of the Wiener systems. The simulation results confirm that the proposed two algorithms are valid and the least squares based iterative algorithm has faster convergence rates than the gradient based iterative algorithm.