Novel Direct and Self-Regulating Approaches to Determine Optimum Growing Multi-Experts Network Structure
This paper presents two novel approaches to determine optimum growing multi-experts network (GMN) structure. The first method called direct method deals with expertise domain and levels in connection with local experts. The growing neural gas (GNG) algorithm is used to cluster the local experts. The...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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2004
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| Online Access: | http://shdl.mmu.edu.my/2434/ http://shdl.mmu.edu.my/2434/1/1706.pdf |
| _version_ | 1848790055304298496 |
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| author | Loo, C.K. Rajeswari, M. Rao, M.V.C. |
| author_facet | Loo, C.K. Rajeswari, M. Rao, M.V.C. |
| author_sort | Loo, C.K. |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | This paper presents two novel approaches to determine optimum growing multi-experts network (GMN) structure. The first method called direct method deals with expertise domain and levels in connection with local experts. The growing neural gas (GNG) algorithm is used to cluster the local experts. The concept of error distribution is used to apportion error among the local experts. After reaching the specified size of the network, redundant experts removal algorithm is invoked to prune the size of the network based on the ranking of the experts. However, GMN is not ergonomic due to too many network control parameters. Therefore, a self-regulating GMN (SGMN) algorithm is proposed. SGMN adopts self-adaptive learning rates for gradient-descent learning rules. In addition, SGMN adopts a more rigorous clustering, method called fully self-organized simplified adaptive resonance theory in a modified form. Experimental results show SGMN obtains comparative or even better performance than GMN in four benchmark examples, with reduced sensitivity to learning parameters setting. Moreover, both GMN and SGMN outperform the other neural networks and statistical models. The efficacy of SGMN is further justified in three industrial applications and a control problem. It provides consistent results besides holding out a profound potential and promise for building a novel type of nonlinear model consisting of several local linear models. |
| first_indexed | 2025-11-14T18:06:32Z |
| format | Article |
| id | mmu-2434 |
| institution | Multimedia University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:06:32Z |
| publishDate | 2004 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-24342011-08-22T02:04:05Z http://shdl.mmu.edu.my/2434/ Novel Direct and Self-Regulating Approaches to Determine Optimum Growing Multi-Experts Network Structure Loo, C.K. Rajeswari, M. Rao, M.V.C. QA75.5-76.95 Electronic computers. Computer science This paper presents two novel approaches to determine optimum growing multi-experts network (GMN) structure. The first method called direct method deals with expertise domain and levels in connection with local experts. The growing neural gas (GNG) algorithm is used to cluster the local experts. The concept of error distribution is used to apportion error among the local experts. After reaching the specified size of the network, redundant experts removal algorithm is invoked to prune the size of the network based on the ranking of the experts. However, GMN is not ergonomic due to too many network control parameters. Therefore, a self-regulating GMN (SGMN) algorithm is proposed. SGMN adopts self-adaptive learning rates for gradient-descent learning rules. In addition, SGMN adopts a more rigorous clustering, method called fully self-organized simplified adaptive resonance theory in a modified form. Experimental results show SGMN obtains comparative or even better performance than GMN in four benchmark examples, with reduced sensitivity to learning parameters setting. Moreover, both GMN and SGMN outperform the other neural networks and statistical models. The efficacy of SGMN is further justified in three industrial applications and a control problem. It provides consistent results besides holding out a profound potential and promise for building a novel type of nonlinear model consisting of several local linear models. 2004-11 Article NonPeerReviewed application/pdf en http://shdl.mmu.edu.my/2434/1/1706.pdf Loo, C.K. and Rajeswari, M. and Rao, M.V.C. (2004) Novel Direct and Self-Regulating Approaches to Determine Optimum Growing Multi-Experts Network Structure. IEEE Transactions on Neural Networks, 15 (6). pp. 1378-1395. ISSN 1045-9227 http://dx.doi.org/10.1109/TNN.2004.837779 doi:10.1109/TNN.2004.837779 doi:10.1109/TNN.2004.837779 |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Loo, C.K. Rajeswari, M. Rao, M.V.C. Novel Direct and Self-Regulating Approaches to Determine Optimum Growing Multi-Experts Network Structure |
| title | Novel Direct and Self-Regulating Approaches to Determine Optimum Growing Multi-Experts Network Structure |
| title_full | Novel Direct and Self-Regulating Approaches to Determine Optimum Growing Multi-Experts Network Structure |
| title_fullStr | Novel Direct and Self-Regulating Approaches to Determine Optimum Growing Multi-Experts Network Structure |
| title_full_unstemmed | Novel Direct and Self-Regulating Approaches to Determine Optimum Growing Multi-Experts Network Structure |
| title_short | Novel Direct and Self-Regulating Approaches to Determine Optimum Growing Multi-Experts Network Structure |
| title_sort | novel direct and self-regulating approaches to determine optimum growing multi-experts network structure |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/2434/ http://shdl.mmu.edu.my/2434/ http://shdl.mmu.edu.my/2434/ http://shdl.mmu.edu.my/2434/1/1706.pdf |