Nonlinear adaptive filtering techniques are widely used for the nonlinearities identification in many applications. This paper proposes a new implementation of the third order RLS Volterra filter based on the decomposition of the input vector. Its performances are evaluated in a typical nonlinear system identification application. Different degrees of nonlinearity for the nonlinear system are considered. Comparations, based on the adaptive filter error, are made in all cases with a linear identifier. The experimental results show that the proposed nonlinear identifier has better performances than the linear one.
An improved spline adaptive filter for nonlinear system identification under impulsive noise environment
