NDLI: Incremental learning algorithm for function approximation and its realization using neural networks

Content Provider	IEEE Xplore Digital Library
Author	Haridy, S.K.
Copyright Year	2004
Description	Author affiliation: Fac. of Eng., South Valley Univ., Aswan, Egypt (Haridy, S.K.)
Abstract	In this paper an incremental learning algorithm for function approximation is presented. The algorithm utilizes the current training pattern to generate an approximately learned function with minimum change in the previously learned one. The algorithm does not store previous learned data for retraining, thus it emulates the biological incremental learning process. The new learned function coefficients are computed using the least squares and the goal attainment optimization methods. The proposed algorithm can be applied to learn systems that operate dynamically in changed environments, such as learning inverse dynamics of robots.
Sponsorship	IEEE Circuits and Syst. Soc. Arab Acad. for Sci. Technol. and Maritime Transport
Starting Page	986
Ending Page	989
File Size	660079
Page Count	4
File Format	PDF
ISBN	0780382943
ISSN	15483746
DOI	10.1109/MWSCAS.2003.1562452
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-12-27
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Approximation algorithms Function approximation Neural networks Least squares approximation Optimization methods Least squares methods Machine learning Learning systems Shape Biology computing
Content Type	Text
Resource Type	Article
Subject	Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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