کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
495788 | 862838 | 2013 | 11 صفحه PDF | دانلود رایگان |
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∞ filter is proposed as the learning algorithm for the neural network to identify the unknown plants. H∞ 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.
Figure optionsDownload as PowerPoint slideHighlights
► NARX model based single layer neural network used for nonlinear system identification.
► Chebyshev polynomials, Legendre polynomials, Local wavelet network for functional expansion of input patterns.
► H∞ filter is being proposed as robust adaptive filtering method for neural network training and weight updation.
Journal: Applied Soft Computing - Volume 13, Issue 7, July 2013, Pages 3324–3334