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DimensionsAsirvadam, V.S. (2008) Adaptive regularizer for recursive neural network training algorithms. In: UNSPECIFIED.
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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....
Abstract
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.
| Item Type: | Conference or Workshop Item (UNSPECIFIED) |
|---|---|
| Additional Information: | cited By 6; Conference of 11th IEEE International Conference on Computational Science and Engineering, CSE Workshops 2008 ; Conference Date: 16 July 2008 Through 18 July 2008; Conference Code:73977 |
| Uncontrolled Keywords: | Neural networks; Recursive functions; Technical presentations, Levenberg-marquardt; Multilayer perceptions; Novel applications; Parameter corrections; Recursive neural networks, Adaptive algorithms |
| Depositing User: | Mr Ahmad Suhairi UTP |
| Date Deposited: | 09 Nov 2023 15:16 |
| Last Modified: | 09 Nov 2023 15:16 |
| URI: | https://khub.utp.edu.my/scholars/id/eprint/423 |
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