By Topic

On estimation of a class of nonlinear systems by the kernel regression estimate

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
$31 $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

1 Author(s)
Krzyzak, A. ; Dept. of Comput. Sci., Concordia Univ., Montreal., Que., Canada

The estimation of a multiple-input single-output discrete Hammerstein system is studied. Such a system contains a nonlinear memoryless subsystem followed by a dynamic linear subsystem. The impulse response of the dynamic linear subsystem is obtained by the correlation method. The main results concern the estimation of the nonlinear memoryless subsystem. No conditions are imposed on the functional form of the nonlinear subsystem, and the nonlinearity is recovered using the kernel regression estimate. The distribution-free pointwise and global convergence of the estimate is demonstrated-that is, no conditions are imposed on the input distribution, and convergence is proven for virtually all nonlinearities. The rates of pointwise as well as global convergence are obtained for all input distributions and for Lipschitz type nonlinearities

Published in:

Information Theory, IEEE Transactions on  (Volume:36 ,  Issue: 1 )