This letter focuses on the parameter estimation of block-oriented Hammerstein nonlinear systems. In order to solve the dimension disaster problem and reduce the computational complexity of the over-parametrization based methods, a parameter separation based multi-innovation stochastic gradient identification algorithm is proposed by using the filtering technique and the multi-innovation identification theory. The proposed method can avoid estimating the redundant parameters and can generate highly accurate parameter estimates. A simulation example is provided to demonstrate its effectiveness.
['Yawen Mao', 'Feng Ding']
A novel parameter separation based identification algorithm for Hammerstein systems