NARX model based nonlinear dynamic system identification using low complexity neural networks and robust H-infinity filter
This paper proposes NARX (nonlinear autoregressive model with exogenous input) model structures with functional expansion of input patterns by using low complexity ANN (artificial neural network) for nonlinear system identification. Chebyshev polynomials, Legendre polynomials, trigonometric expansions using sine and cosine functions as well as wavelet basis functions are used for the functional expansion of input patterns. The past input and output samples are modeled as a nonlinear NARX process and robust H-infinity filter is proposed as the learning algorithm for the neural network to identify the unknown plants. H-infinity filtering approach is based on the state space modeling of model parameters and evaluation of Jacobian matrices. This approach is the robustification of Kalman filter which exhibits robust characteristics and fast convergence properties. Comparison results for different nonlinear dynamic plants with forgetting factor recursive least square (FFRLS) and extended Kalman filter (EKF) algorithms demonstrate the effectiveness of the proposed approach. (C) 2013 Elsevier B.V. All rights reserved.
narx model h-infinity filter chebyshev polynomials legendre polynomials ffrls ekf wavelet neural network