Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1707383 | Applied Mathematics Letters | 2016 | 7 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Yawen Mao, Feng Ding,