Modeling a Weakly Nonlinear System Using Second Order Hammerstein Series

Lining Tan; Hongguang Ma; Libin Lu
March 2013
Advances in Information Sciences & Service Sciences;Mar2013, Vol. 5 Issue 6, p53
Academic Journal
A broad class of weakly nonlinear systems can be modeled by second order Volterra models. However, the parametric complexity limits their use in practical engineering applications. In order to solve this problem, we proposed the use of second order Hammerstein series models, which could lead to a trade-off between computational cost and model accuracy. To extract such model from measured input-output data, a nonparametric identification method was presented. In this method, both the minimum mean square error criterion and polyspectrum up to fourth order were used to estimate the model. Furthermore, implementation of this method was describe and validated on simulated system. Simulations show that the proposed method is of excellent generalization ability and robustness against output noise. Moreover, it leads to significant reductions in computational requirements.


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