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Robust Adaptive Identification of Nonlinear System Using Neural Network
Author(s) -
Song Q.,
Hu W.J.,
Soh Y.C.
Publication year - 2001
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1111/j.1934-6093.2001.tb00054.x
Subject(s) - upper and lower bounds , nonlinear system , control theory (sociology) , convergence (economics) , dead zone , artificial neural network , adaptive control , computer science , system identification , norm (philosophy) , identification scheme , disturbance (geology) , divergence (linguistics) , identification (biology) , mathematics , artificial intelligence , law , control (management) , philosophy , process (computing) , database , economic growth , mathematical analysis , linguistics , oceanography , biology , operating system , paleontology , political science , measure (data warehouse) , physics , economics , geology , botany , quantum mechanics
It is well known that disturbance can cause divergence of neural networks in the identification of nonlinear systems. Sufficient conditions using so‐called modified algorithms are available to provide guaranteed convergence for adaptive system. They are dead zone scheme, adaptive law modification, and σ‐modification. These schemes normally require knowledge of the upper bound of the disturbance. In this paper, a robust weighttuning algorithm is used to train the multi‐layered neural network with an adaptive dead zone scheme. The proposed robust adaptive algorithm does not require knowledge of either the upper bound of the disturbance or the bound on the norm of the estimate parameter. A complete convergence proof is provided based on Lyapunov theorem to deal with the nonlinear system. Simulation results are presented to show good perfor‐mance of the algorithm.