Convergence index for BPN training
The Back Propagation Network (BPN) is one of the most widely used neural networks. It has a distinct training phase and then it is put to use. It is observed that certain training sets would Get trained and certain others would not get trained in a BPN. This is an inherent property of the training s...
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| Format: | Article |
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2003
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| Online Access: | http://shdl.mmu.edu.my/2536/ |
| _version_ | 1848790082204467200 |
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| author | Gunasekaran, S |
| author_facet | Gunasekaran, S |
| author_sort | Gunasekaran, S |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | The Back Propagation Network (BPN) is one of the most widely used neural networks. It has a distinct training phase and then it is put to use. It is observed that certain training sets would Get trained and certain others would not get trained in a BPN. This is an inherent property of the training set and the BPN architecture. This paper proposes a new training index that may be evaluated after a certain number of training epochs and would indicate the ability of the BPN to train for the existing topology and training set. The index is evaluated for several logic gates and the results are presented. (C) 2003 Elsevier B.V. All rights reserved. |
| first_indexed | 2025-11-14T18:06:57Z |
| format | Article |
| id | mmu-2536 |
| institution | Multimedia University |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:06:57Z |
| publishDate | 2003 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-25362011-08-22T06:05:10Z http://shdl.mmu.edu.my/2536/ Convergence index for BPN training Gunasekaran, S QA75.5-76.95 Electronic computers. Computer science The Back Propagation Network (BPN) is one of the most widely used neural networks. It has a distinct training phase and then it is put to use. It is observed that certain training sets would Get trained and certain others would not get trained in a BPN. This is an inherent property of the training set and the BPN architecture. This paper proposes a new training index that may be evaluated after a certain number of training epochs and would indicate the ability of the BPN to train for the existing topology and training set. The index is evaluated for several logic gates and the results are presented. (C) 2003 Elsevier B.V. All rights reserved. 2003-10 Article NonPeerReviewed Gunasekaran, S (2003) Convergence index for BPN training. Neurocomputing, 55 (3-4). pp. 711-719. ISSN 09252312 http://dx.doi.org/10.1016/S0925-2312(03)00368-0 doi:10.1016/S0925-2312(03)00368-0 doi:10.1016/S0925-2312(03)00368-0 |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Gunasekaran, S Convergence index for BPN training |
| title | Convergence index for BPN training |
| title_full | Convergence index for BPN training |
| title_fullStr | Convergence index for BPN training |
| title_full_unstemmed | Convergence index for BPN training |
| title_short | Convergence index for BPN training |
| title_sort | convergence index for bpn training |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/2536/ http://shdl.mmu.edu.my/2536/ http://shdl.mmu.edu.my/2536/ |