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 |
| Published: |
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 |
| Summary: | 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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