A time-varying forgetting factor stochastic gradient combined with Kalman filter algorithm for parameter identification of dynamic systems

Parameter estimation problem of a class of observer canonical state-space system is considered in this paper. By means of the property of the shift operator, the space-state model is transformed into the input–output representations. Then, a time-varying forgetting factor stochastic gradient combined with Kalman filter algorithm is proposed. The proposed algorithm is based on interactively estimating unknown parameters to achieve all the parameters identification of the system. A numerical example is provided to verify the effectiveness of the proposed algorithm.