A new model with neural network structure for nonlinear identification

In this paper, a new model for nonlinear system identification is presented. It consists of two parts: a linear part and a static nonlinear output part. The linear part is a linear combination of the model's outputs, and the static nonlinear function maps the output of the linear part to the model's output. This model can be applied to represent a relatively large class of nonlinear dynamic systems with fading memory. Nonlinear system identification with this new model is applied to two simulation examples of a discrete-time system and a complicated missile dynamics to demonstrate the performance and efficiency of the proposed method.