Identification of Hammerstein model using functional link artificial neural network

• A novel FLANN based method for identifying Hammerstein model is proposed.
• FLANN is used to represent the static nonlinear part of Hammerstein model.
• L–M algorithm is derived to learn the weights of FLANN.
• Linear and nonlinear parts are identified separately using specified input signal.
• Numerical results verify the effectiveness of the proposed method.

In this paper, a novel algorithm is developed for identifying Hammerstein model. The static nonlinear function is characterized by function link artificial neural network (FLANN) and the linear dynamic subsystem by an ARMA model. The utilization of FLANN can not only result in a simple and effective representation of static nonlinearity but also simplify the learning algorithm. A two-step procedure is adopted to identify Hammerstein model by using a specially designed input signal, which separates the identification of linear part from that of nonlinear part. Levenberg–Marquart algorithm is used to learn the weights of FLANN. Simulation examples demonstrate the effectiveness of the proposed method.