Stochastic gradient parameter estimation of input nonlinear systems using the filtering technique

For input nonlinear output error moving average systems with a two-segment piecewise nonlinearity, a data filtering based stochastic gradient algorithm is developed to estimate the parameters of this nonlinear system based on the data filtering. The basic idea is to combine the key-term separation principle and the data filtering technique, and to decompose the identification model into two models. The simulation results indicate that the proposed algorithm can give more accurate parameter estimates than existing extended stochastic gradient algorithm.