Decomposition based recursive identification algorithms for bilinear-parameter models

This paper presents two recursive identification algorithms for bilinear-parameter models: a decomposition based stochastic gradient algorithm and a decomposition based recursive least squares algorithm. The key is to decompose a bilinear-parameter model into two fictitious subsystems, and to identify the parameters of each subsystem by replacing the unknown variables in the information vectors with their estimates. The simulation results show the performances the proposed algorithms.