By Topic

Orthogonal wavelet neural networks applying to identification of Wiener model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Yong Fang ; Dept. of Electron. Eng., Hong Kong Polytech., Kowloon, Hong Kong ; T. W. S. Chow

In this paper, an orthogonal wavelet-based neural network (OWNN) is proposed. In the proposed OWNN both the orthogonal scaling functions and the corresponding mother wavelets are combined as the nonlinear activation function. The OWNN is applied to identify a Wiener-type cascade dynamical model. A linear autoregressive moving average (ARMA) model is used as the dynamic subsystems and the OWNN is employed as the nonlinear static subsystem. A Wiener model identification algorithm is formed by combining the proposed OWNN with the conventional least squares method

Published in:

IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications  (Volume:47 ,  Issue: 4 )