Robust extreme learning machine neural approach for uncertain nonlinear hyper‐chaotic system identification

This paper proposes a novel nonlinearly parameterized advanced single‐hidden layer neural extreme learning machine (ASHLN‐ELM) model in which the hidden and output weighting values are simultaneously updated using adaptively robust rules that are implemented based on Lyapunov stability principle. The proposed scheme guarantees the fast convergence speed of the state‐estimation residual errors bounded to null regarding to the influence of time‐varied disturbances. Additionally, proposed method needs no any knowledge related to desired weighting values or required approximating error. Typical uncertain hyper‐chaotic benchmark systems are used as to verify the new ASHLN‐ELM approach and to demonstrate the efficiency and the robustness of proposed method.