Nonparametric identification of affine nonlinear systems

Recursive nonparametric identification of affine nonlinear systems is considered in this paper. First, by using Markov chain approach, the geometric ergodicity is established for the affine nonlinear system under suitable conditions with the help of the concept of Q-geometric ergodicity. Then recursive local constant kernel regression estimator is proposed for estimating the values of the nonlinear functions at fixed points. It is proved that the estimate converges to the true value with probability one. Finally a simulation example is provided to justify the theoretical analysis.