Journal Article10.1023/B:STCO.0000039483.71153.8D
Automatic polynomial wavelet regression
Thomas C. M. Lee,Hee-Seok Oh +1 more
TL;DR: This paper proposes two automatic methods for making the choice of the order of the low-order polynomial, as well as the wavelet thresholding value, and evaluation of these two methods is evaluated via numerical experiments.
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Abstract: In Oh, Naveau and Lee (2001) a simple method is proposed for reducing the bias at the boundaries for wavelet thresholding regression. The idea is to model the regression function as a sum of wavelet basis functions and a low-order polynomial. The latter is expected to account for the boundary problem. Practical implementation of this method requires the choice of the order of the low-order polynomial, as well as the wavelet thresholding value. This paper proposes two automatic methods for making such choices. Finite sample performances of these two methods are evaluated via numerical experiments.
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Citations
Refinements of damage detection methods based on wavelet analysis of dynamical shapes
TL;DR: This manuscript aims at illustrating significant refinements concerning the use of wavelets, when these latter are used in the guise of continuous wavelet transforms (CWT) for identifying damage on transversally vibrating structural components (e.g. beams, plates and shells).
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A Hybrid Optimized Model of Adaptive Neuro-Fuzzy Inference System, Recurrent Kalman Filter and Neuro-Wavelet for Wind Power Forecasting Driven by DFIG
TL;DR: In this article , a hybrid optimized model of Adaptive Neuro-Fuzzy Inference System (ANFIS), Recurrent Kalman Filter (RKF), and Neuro-Wavelet (WNN) for wind power forecasting driven by doubly fed induction generator (DFIG).
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A Hybrid Optimized Model of Adaptive Neuro-Fuzzy Inference System, Recurrent Kalman Filter and Neuro-Wavelet for Wind Power Forecasting Driven by DFIG
TL;DR: In this paper, a hybrid optimized model of Adaptive Neuro-Fuzzy Inference System (ANFIS), Recurrent Kalman Filter (RKF), and Neuro-Wavelet (WNN) for wind power forecasting driven by doubly fed induction generator (DFIG).
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Wavelet Amendment of Polynomial Models in Hammerstein Systems Identification
TL;DR: A new wavelet algorithm for on-line improvement of an existing polynomial model of nonlinearity in a Hammerstein system is proposed and its properties are examined.
Hybrid local polynomial wavelet shrinkage: wavelet regression with automatic boundary adjustment
Hee-Seok Oh,Thomas C. M. Lee +1 more
TL;DR: Simulation results from both the univariate and bivariate settings provide strong evidence that the proposed method is extremely effective in terms of correcting boundary bias.
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