A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time series and dependent variables
Physica D
Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
A new class of wavelet networks for nonlinear system identification
IEEE Transactions on Neural Networks
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Wavelet based non-parametric additive NARX models are proposed for nonlinear input-output system identification. By expanding each functional component of the non-parametric NARX model into wavelet multiresolution expansions, the non-parametric estimation problem becomes a linear-in-the-parameters problem, and least-squares-based methods such as the orthogonal forward regression (OFR) approach, coupled with model size determination criteria, can be used to select the model terms and estimate the parameters. Wavelet based additive models, combined with model order determination and variable selection approaches, are capable of handling problems of high dimensionality.