Adaptive general regression neural network for modelling of dynamic plants


T.L., Seng and M. Khalid, and R. Yusof, (1999) Adaptive general regression neural network for modelling of dynamic plants. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 213 (14). pp. 275-287. ISSN 0959-6518

[img] Text
Adaptive general regression neural network for modelling of dynamic plants.pdf
Restricted to Repository staff only

Download (277kB)


This paper proposes an integrated general regression neural network (GRNN) adaptation scheme for dynamic plant modelling. The scheme can be used in a noisy and dynamic environment for on-line process control. It possesses several distinguished features compared with the original GRNN proposed by Specht, such as a flexible pattern nodes add-in and delete-off mechanism, a dynamic initial sigma assignment using a non-statistical method, automatic target adjustment and sigma tuning. These adaptation strategies are formulated on the basis of the inherent advantageous features found in GRNN, such as highly localized pattern nodes, good interpolation capability and instantaneous learning. Good modelling performance was 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. In this paper, analysis of the effects of some of the adaptation parameters involving a non-linear 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.

Item Type: Article
Subjects: T Technology > TJ Mechanical Engineering and Machinery
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 25 Oct 2013 04:38
Last Modified: 25 Oct 2013 09:03


Downloads per month over past year

View ItemEdit (login required)