NDLI: Two Types of Haar Wavelet Neural Networks for Nonlinear System Identification

Content Provider	Springer Nature Link
Author	Cordova, Juan Yu, Wen
Copyright Year	2012
Abstract	Since wavelet transform uses the multi-scale (or multi-resolution) techniques for time series, wavelet transform is suitable for modeling complex signals. Haar wavelet transform is the most commonly used and the simplest one. The Haar wavelet neural network (HWNN) applies the Harr wavelet transform as active functions. It is easy for HWNN to model a nonlinear system at multiple time scales and sudden transitions. In this paper, two types of HWNN, feedforward and recurrent wavelet neural networks, are used to model discrete-time nonlinear systems, which are in the forms of the NARMAX model and state-space model. We first propose an optimal method to determine the structure of HWNN. Then two stable learning algorithms are given for the shifting and broadening coefficients of the wavelet functions. The stability of the identification procedures is proven.
Starting Page	283
Ending Page	300
Page Count	18
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	35
Issue Number	3
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2012-03-15
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Wavelet neural networks Modeling Stability Statistical Physics, Dynamical Systems and Complexity Computational Intelligence Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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