Abstract
Abstract In this paper, a wavelet-based neural network (WNN) is introduced for continuous nonlinear system identification. The adaptive weight learning laws are derived based on Lyapunov stability theory for static and dynamical system identification respectively. It is proved that the identification error will converge to zero and the weight will be bounded in the case of no modeling error. Simulation results demonstrate the effectiveness of the proposed identification methodology.
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