System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
A bibliography on nonlinear system identification
Signal Processing - Special section on digital signal processing for multimedia communications and services
On-line system identification of complex systems using Chebyshev neural networks
Applied Soft Computing
Evolutionary Multi-Model Estimators for ARMA System Modeling and Time Series Prediction
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Expert Systems with Applications: An International Journal
Improved identification of Hammerstein plants using new CPSO and IPSO algorithms
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Robust estimation of a single complex sinusoid in whitenoise-H∞ filtering approach
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Identification of nonlinear dynamic systems using functional linkartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear dynamic system identification using Chebyshev functionallink artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Brief Parameter identification of a class of Hammerstein plants
Automatica (Journal of IFAC)
Mathematical and Computer Modelling: An International Journal
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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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.