Adaptive regularizer for recursive neural network training algorithms
Adaptive Marquardt parameter correction techniques are tested for recursive Levenberg-Marquardt (RLM) and proposed novel application on decomposed recursive Levenberg Marquardt (DRLM) algorithms. The adaptive Marquardt correction is based on recursive moving-window residual. Experiment results show...
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| Format: | Conference or Workshop Item |
| Language: | English |
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2008
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| Online Access: | http://scholars.utp.edu.my/id/eprint/259/ http://scholars.utp.edu.my/id/eprint/259/1/paper.pdf |
| _version_ | 1848658944499646464 |
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| author | Asirvadam, Vijanth Sagayan |
| author_facet | Asirvadam, Vijanth Sagayan |
| author_sort | Asirvadam, Vijanth Sagayan |
| building | UTP Institutional Repository |
| collection | Online Access |
| description | Adaptive Marquardt parameter correction techniques are tested for recursive Levenberg-Marquardt (RLM) and proposed novel application on decomposed recursive Levenberg Marquardt (DRLM) algorithms. The adaptive Marquardt correction is based on recursive moving-window residual. Experiment results show superior convergence using decomposed approach and a slight improvement in performance by adopting the adaptive Marquardt correction on a fixed size multilayer perceptions (MLP) network. © 2008 IEEE.
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| first_indexed | 2025-11-13T07:22:35Z |
| format | Conference or Workshop Item |
| id | oai:scholars.utp.edu.my:259 |
| institution | Universiti Teknologi Petronas |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-13T07:22:35Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:scholars.utp.edu.my:2592017-01-19T08:26:24Z http://scholars.utp.edu.my/id/eprint/259/ Adaptive regularizer for recursive neural network training algorithms Asirvadam, Vijanth Sagayan TK Electrical engineering. Electronics Nuclear engineering Adaptive Marquardt parameter correction techniques are tested for recursive Levenberg-Marquardt (RLM) and proposed novel application on decomposed recursive Levenberg Marquardt (DRLM) algorithms. The adaptive Marquardt correction is based on recursive moving-window residual. Experiment results show superior convergence using decomposed approach and a slight improvement in performance by adopting the adaptive Marquardt correction on a fixed size multilayer perceptions (MLP) network. © 2008 IEEE. 2008 Conference or Workshop Item NonPeerReviewed application/pdf en http://scholars.utp.edu.my/id/eprint/259/1/paper.pdf Asirvadam, Vijanth Sagayan (2008) Adaptive regularizer for recursive neural network training algorithms. In: 11th IEEE International Conference on Computational Science and Engineering, CSE Workshops 2008, 16 July 2008 through 18 July 2008, Sao Paulo, SP. http://www.scopus.com/inward/record.url?eid=2-s2.0-55849101672&partnerID=40&md5=269731419c26dfaff29ed744ee54d2b9 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Asirvadam, Vijanth Sagayan Adaptive regularizer for recursive neural network training algorithms |
| title | Adaptive regularizer for recursive neural network training algorithms
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| title_full | Adaptive regularizer for recursive neural network training algorithms
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| title_fullStr | Adaptive regularizer for recursive neural network training algorithms
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| title_full_unstemmed | Adaptive regularizer for recursive neural network training algorithms
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| title_short | Adaptive regularizer for recursive neural network training algorithms
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| title_sort | adaptive regularizer for recursive neural network training algorithms |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://scholars.utp.edu.my/id/eprint/259/ http://scholars.utp.edu.my/id/eprint/259/ http://scholars.utp.edu.my/id/eprint/259/1/paper.pdf |