By Topic

A Non-linear Function Approximation from Small Samples Based on Nadaraya-Watson Kernel Regression

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

5 Author(s)
Mohd Ibrahim Shapiai ; Centre of Artificial Intell. & Robot. (CAIRO), Univ. Teknol. Malaysia (UTM), Kuala Lumpur, Malaysia ; Zuwairie Ibrahim ; Marzuki Khalid ; Lee Wen Jau
more authors

Solving function approximation problem is to appropriately find the relationship between dependent variable and independent variable(s). Function approximation algorithms normally require sufficient amount of samples to approximate a function. However, insufficient samples may result in unsatisfactory prediction to any function approximation algorithms. It is due to the failure of the function approximation algorithms to fill the information gap between the available and very limited samples. In this study, a function approximation algorithm which is based on Nadaraya-Watson Kernel Regression (NWKR) is proposed for approximating a non-linear function with small samples. Gaussian function is chosen as a kernel function for this study. The results show that the NWKR is effective in the case where the target function is non-linear and the given training sample is small. The performance of the NWKR is compared with other existing function approximation algorithms, such as artificial neural network.

Published in:

Computational Intelligence, Communication Systems and Networks (CICSyN), 2010 Second International Conference on

Date of Conference:

28-30 July 2010