Neural identification based on sliding mode observer

In this paper, a new on-line neural identification method is presented. The identified nonlinear systems are partial-state measurement. Their inner states, parameters and structures are unknown. The design is based on the combination of a sliding mode observer and a neuro identifier. First, a sliding mode observer, which does not need any information of the nonlinear system, is applied to get the full states. Then a dynamic multilayer neural network is used to identify the whole nonlinear system. The main contributions of this paper are: (1) a new observer based identification algorithm is proposed; (2) a stable learning algorithm for the neuro identifier is given.