Identification of nonlinear systems with time-varying parameters using a sliding-neural network observer
In this paper, a new method for the identification of nonlinear systems with time-varying parameters using a sliding-neural network observer is investigated. The proof of the finite-time convergence of the estimates to their true values is achieved using Lyapunov arguments and sliding mode theories. An application example illustrated the effectiveness of the approach and the obtained results show high convergence rate and very satisfactory parameter estimation accuracy. The computing results under noisy condition also demonstrate that good state and parameter estimation can be achieved despite the disturbance (noise) in the system. The reduced number of hidden units and the small transient period demonstrate that the proposed method can be easily implementable in real-time.
time-varying parameter state/parameter estimation output equivalent injection radial basis function learning algorithms