An improved on-line neuro-identification scheme

In this paper, an on-line identification scheme is proposed to enhance the residual state error performance in face of disturbances. The proposed scheme is based on an e₁-modification adaptive law for the weights to approximate the unknown nonlinearities with bounded error. Besides, an identification model with feedback is introduced to improve the state error performance. The feedback is based on a bounding function to estimate an upper bound for the disturbances. Via an adaptive bounding technique and Lyapunov methods, it is proved that the residual state error performance is practically immune to disturbances. To validate the theoretical results, the identification of a four-order generalized Lü hyperchaotic system is performed.