Nonlinear time-varying system identification based on time-varying NARMA model

Introducing time variable into the NARMA (Nonlinear Auto Regressive Moving Average) model make it expand to time-varying NARMA model. The nonlinear function of the model can be expanded to a polynomial of input and output using Taylor expansion, and the polynomial time-varying NARMA model that is linear to the parameters is obtained. Using base sequences to fit the time-varying parameters of the model, the nonlinear time-varying system is transformed into a time-invariant linear one, the parameters of which can be estimated by recursive least square algorithm. The results of simulation examples show that the identification accuracy and the computational complexity of this method are better than those of wavelet neural network method.