Dynamic tunneling based regularization in feedforward neural networks
This paper presents a new regularization method based on dynamic tunneling for enhancing generalization capability of multilayered neural networks. The proposed method enables escape through undesired sub-optimal solutions on the composite error surface by means of dynamic tunneling. Undesired sub-o...
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| Format: | Article |
| Language: | English |
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2001
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| Online Access: | http://shdl.mmu.edu.my/2685/ http://shdl.mmu.edu.my/2685/1/1926.pdf |
| _version_ | 1848790123139825664 |
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| author | Singh, Y.P. RoyChowdhury, Pinaki |
| author_facet | Singh, Y.P. RoyChowdhury, Pinaki |
| author_sort | Singh, Y.P. |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | This paper presents a new regularization method based on dynamic tunneling for enhancing generalization capability of multilayered neural networks. The proposed method enables escape through undesired sub-optimal solutions on the composite error surface by means of dynamic tunneling. Undesired sub-optimal solutions may be increased or introduced from regularized objective function. Hence, the proposed method is capable of enhancing the regularization property without getting stuck at sub-optimal values in search space. The regularization property and escape from the sub-optimal values have been demonstrated through computer simulations on two examples. (C) 2001 Elsevier Science B.V. All rights reserved. |
| first_indexed | 2025-11-14T18:07:36Z |
| format | Article |
| id | mmu-2685 |
| institution | Multimedia University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:07:36Z |
| publishDate | 2001 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-26852014-02-06T04:22:26Z http://shdl.mmu.edu.my/2685/ Dynamic tunneling based regularization in feedforward neural networks Singh, Y.P. RoyChowdhury, Pinaki QA75.5-76.95 Electronic computers. Computer science This paper presents a new regularization method based on dynamic tunneling for enhancing generalization capability of multilayered neural networks. The proposed method enables escape through undesired sub-optimal solutions on the composite error surface by means of dynamic tunneling. Undesired sub-optimal solutions may be increased or introduced from regularized objective function. Hence, the proposed method is capable of enhancing the regularization property without getting stuck at sub-optimal values in search space. The regularization property and escape from the sub-optimal values have been demonstrated through computer simulations on two examples. (C) 2001 Elsevier Science B.V. All rights reserved. 2001-09 Article NonPeerReviewed text en http://shdl.mmu.edu.my/2685/1/1926.pdf Singh, Y.P. and RoyChowdhury, Pinaki (2001) Dynamic tunneling based regularization in feedforward neural networks. Artificial Intelligence, 131 (1-2). pp. 55-71. ISSN 00043702 http://dx.doi.org/10.1016/S0004-3702(01)00112-6 doi:10.1016/S0004-3702(01)00112-6 doi:10.1016/S0004-3702(01)00112-6 |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Singh, Y.P. RoyChowdhury, Pinaki Dynamic tunneling based regularization in feedforward neural networks |
| title | Dynamic tunneling based regularization in feedforward neural networks |
| title_full | Dynamic tunneling based regularization in feedforward neural networks |
| title_fullStr | Dynamic tunneling based regularization in feedforward neural networks |
| title_full_unstemmed | Dynamic tunneling based regularization in feedforward neural networks |
| title_short | Dynamic tunneling based regularization in feedforward neural networks |
| title_sort | dynamic tunneling based regularization in feedforward neural networks |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/2685/ http://shdl.mmu.edu.my/2685/ http://shdl.mmu.edu.my/2685/ http://shdl.mmu.edu.my/2685/1/1926.pdf |