A new class of wavelet networks for nonlinear system identification
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TLDR
An efficient model term selection approach based upon a forward orthogonal least squares (OLS) algorithm and the error reduction ratio (ERR) is applied to solve the linear-in-the-parameters problem in the present study.Abstract:
A new class of wavelet networks (WNs) is proposed for nonlinear system identification. In the new networks, the model structure for a high-dimensional system is chosen to be a superimposition of a number of functions with fewer variables. By expanding each function using truncated wavelet decompositions, the multivariate nonlinear networks can be converted into linear-in-the-parameter regressions, which can be solved using least-squares type methods. An efficient model term selection approach based upon a forward orthogonal least squares (OLS) algorithm and the error reduction ratio (ERR) is applied to solve the linear-in-the-parameters problem in the present study. The main advantage of the new WN is that it exploits the attractive features of multiscale wavelet decompositions and the capability of traditional neural networks. By adopting the analysis of variance (ANOVA) expansion, WNs can now handle nonlinear identification problems in high dimensions.read more
Citations
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New Delay-Dependent Stability Criteria for Neural Networks With Time-Varying Delay
Yong He,Guo-Ping Liu,David Rees +2 more
TL;DR: A new method is proposed for stability analysis of neural networks (NNs) with a time-varying delay by considering the additional useful terms, which were ignored in previous methods, when estimating the upper bound of the derivative of Lyapunov functionals and introducing the new free-weighting matrices.
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AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network
Kanna Bhaskar,Sri Niwas Singh +1 more
TL;DR: In this article, a statistical-based wind power forecasting without using numerical weather prediction (NWP) inputs is carried out in this work, which consists of two stages, in stage-I, wavelet decomposition of wind series was carried out and adaptive wavelet neural network (AWNN) was used to regress upon each decomposed signal, to predict wind speed up to 30 h ahead.
Journal ArticleDOI
An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets
TL;DR: In this paper, an adaptive wavelet neural network (AWNN) was proposed for short-term price forecasting in the electricity markets, where a commonly used Mexican hat wavelet has been chosen as the activation function for hidden-layer neurons of feed-forward neural network.
Journal ArticleDOI
Wavelet Adaptive Backstepping Control for a Class of Nonlinear Systems
TL;DR: Simulation results verify that the proposed WABC can achieve favorable tracking performance by incorporating of WNN identification, adaptive backstepping control, and L2 robust control techniques.
Journal ArticleDOI
Wavelet neural networks: A practical guide
TL;DR: This study presents a complete statistical model identification framework in order to apply WNs in various applications and shows that the proposed algorithms produce stable and robust results indicating that the framework can be applied inVarious applications.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Book
A wavelet tour of signal processing
TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Book
Ten lectures on wavelets
TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Journal ArticleDOI
Ten Lectures on Wavelets
TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.