Global Parameter Estimation of Hammerstein Systems with Polynomial Nonlinearity

Abstract

The parameter estimation problem of Hammerstein systems with polynomial nonlinearity is studied. An algorithm is proposed to overcome the problem of possible wrong convergence in Maximum Likelihood methods. The new algorithm provides a unique set of estimates for a given input and output data record corrupted by noise. The effectiveness and accuracy of the method are examined via numerical simulations.