NDLI: Adaptive GRNN for the modelling of dynamic plants

Content Provider	IEEE Xplore Digital Library
Author	Teo Lian Seng Khalid, M. Yusof, R.
Copyright Year	2002
Description	Author affiliation: Centre for Artificial Intelligence & Robotics, Univ. Technol. Malaysia, Malaysia (Teo Lian Seng; Khalid, M.; Yusof, R.)
Abstract	An integrated General Regression Neural Network (GRNN) adaptation scheme for dynamic plant modelling is proposed in this paper. It possesses several improved features compared to the original GRNN proposed by Specht (1991), such as flexible pattern nodes add-in and delete-off mechanism, dynamic initial sigma assignment using a nonstatistical method, automatic target adjustment and sigma tuning. These adaptation strategies are formulated based on the inherent advantageous features found in GRNN, such as highly localised pattern nodes, good interpolation capability, instantaneous learning. Good modelling performance is obtained when the GRNN is tested on a linear plant in a noisy environment. It performs better than the well-known extended recursive least squares identification algorithm. Analysis on the effects of some of the adaptation parameters involving a nonlinear plant is also investigated. The results show that the proposed methodology is computationally efficient and exhibits several attractive features such as fast learning, flexible network sizing and good robustness, which are suitable for the construction of estimators or predictors for many model-based adaptive control strategies.
Starting Page	217
Ending Page	222
File Size	421646
Page Count	6
File Format	PDF
ISBN	078037620X
ISSN	21589860
DOI	10.1109/ISIC.2002.1157765
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-10-30
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Statistical analysis Working environment noise Least squares methods Adaptive control Interpolation Testing Computer networks Robust control Predictive models
Content Type	Text
Resource Type	Article

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