Identification for Hammerstein models based on a modified least angle regression algorithm
For the identification of a class of Hammerstein models with unknown time-delays and orders,this paper pro-poses a sparse system identification method based on the absolute angle stopping criteria least angle regression(AS-LAR)algorithm,which can simultaneously estimate the time-delays,orders and parameters of the Hammerstein model.Firstly,a high-dimensional sparse identification model is derived by introducing a maximum nonlinear order and a maximum input regression length.Then a new absolute angle stopping criteria is presented to modify the least angle regression algorithm,and the sparse parameter vector is identified based on the new AS-LAR algorithm.Finally,the time-delays and system orders are estimated based on the sparse structure of the estimated parameter vector,and the parameters of the nonlinear and linear part are extracted and separated from the estimated parameter vector.A numerical simulation and a water tank example show that the proposed algorithm is effective and has the features of high accuracy of parameter estimation,low computational effort and fast speed.
Hammerstein modelsparse system identificationleast angle regressive algorithmmodel selection criteri-ontime-delay estimation
万方数据
维普
