Parameter estimation for nonlinear dynamical adjustment models

A recursive generalized least squares algorithm and a filtering based least squares algorithm are developed for input nonlinear dynamical adjustment models with memoryless nonlinear blocks followed by linear dynamical blocks. The basic idea is to use the filtering technique and to replace the unknown terms in the information vectors with their estimates. The simulation results show the performance of the proposed algorithms.