Adaptive GRNN for the modelling of dynamic plants
An integrated General Regression Neural Network (GRNN) adaptation scheme for dynamic plant modelling is proposed in this paper. It possesses several distinguished features compared to the original GRNN proposed by Specht [1], such as flexible pattern nodes add-in and delete-off mechanism, dynamic in...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2002
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/7326/ http://eprints.utm.my/7326/1/YusofRubiyah2002_Adaptive_GRNN_Modelling_Dynamic_Plants.pdf |
| _version_ | 1848891446991519744 |
|---|---|
| author | Yusof, Rubiyah Khalid, Marzuki Teo, Lian Seng |
| author_facet | Yusof, Rubiyah Khalid, Marzuki Teo, Lian Seng |
| author_sort | Yusof, Rubiyah |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | An integrated General Regression Neural Network (GRNN) adaptation scheme for dynamic plant modelling is proposed in this paper. It possesses several distinguished features compared to the original GRNN proposed by Specht [1], such as flexible pattern nodes add-in and delete-off mechanism, dynamic initial sigma assignment using non-statistical 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, etc.. 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 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. |
| first_indexed | 2025-11-15T20:58:06Z |
| format | Conference or Workshop Item |
| id | utm-7326 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:58:06Z |
| publishDate | 2002 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-73262017-09-11T02:48:09Z http://eprints.utm.my/7326/ Adaptive GRNN for the modelling of dynamic plants Yusof, Rubiyah Khalid, Marzuki Teo, Lian Seng TK Electrical engineering. Electronics Nuclear engineering An integrated General Regression Neural Network (GRNN) adaptation scheme for dynamic plant modelling is proposed in this paper. It possesses several distinguished features compared to the original GRNN proposed by Specht [1], such as flexible pattern nodes add-in and delete-off mechanism, dynamic initial sigma assignment using non-statistical 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, etc.. 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 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. 2002 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/7326/1/YusofRubiyah2002_Adaptive_GRNN_Modelling_Dynamic_Plants.pdf Yusof, Rubiyah and Khalid, Marzuki and Teo, Lian Seng (2002) Adaptive GRNN for the modelling of dynamic plants. In: Proceeding of the 2002 IEEE International Symposium on Intelligent Control, 27th-30th October 2002, Vancouver, Canada. https://dx.doi.org/10.1109/ISIC.2002.1157765 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Yusof, Rubiyah Khalid, Marzuki Teo, Lian Seng Adaptive GRNN for the modelling of dynamic plants |
| title | Adaptive GRNN for the modelling of dynamic plants |
| title_full | Adaptive GRNN for the modelling of dynamic plants |
| title_fullStr | Adaptive GRNN for the modelling of dynamic plants |
| title_full_unstemmed | Adaptive GRNN for the modelling of dynamic plants |
| title_short | Adaptive GRNN for the modelling of dynamic plants |
| title_sort | adaptive grnn for the modelling of dynamic plants |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/7326/ http://eprints.utm.my/7326/ http://eprints.utm.my/7326/1/YusofRubiyah2002_Adaptive_GRNN_Modelling_Dynamic_Plants.pdf |