Novel identification algorithms for Hammerstein systems in ill-conditioned situations

Abstract

Two algorithms named Recursive Least Square algorithm for Ill-Conditioned situations (RLS-IC) and Multi Innovation Stochastic Gradient algorithm for Ill-Conditioned situations (MISG-IC) are proposed based on multi-innovation identification theory, least squares, stochastic gradient and singular value decomposition to identify Hammerstein systems in ill-conditioned situations. In ill-conditioned situations, identification algorithms based on classic least-squares and stochastic gradient lead to uncertain estimates and may be ineffective. Simulation examples show that the proposed algorithms are suitable for parameter estimation in Hammerstein nonlinear systems with ill-conditioned situations.