Observer-based neuro identifier

A new online identification method is presented. The identified nonlinear s ystems have partial-state measurement. Their inner states, parameters and s tructures are unknown. The design is based on the combination of a model-fr ee state observer and a neuro identifier. First, a sliding mode observer, w hich does not need any information about the nonlinear system, is applied t o obtain the full states. A dynamic multilayer neural network is then used to identify the whole nonlinear system. The main contributions of the paper are: a new observer-based identification algorithm is proposed; and a stab le learning algorithm for the neuro identifier is given.