Scholarly PublicationAsirvadam, 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.
![[thumbnail of Adaptive regularizer for recursive neural network training algorithms]](http://scholars.utp.edu.my/style/images/fileicons/application_pdf.png)
PDF (Adaptive regularizer for recursive neural network training algorithms)
paper.pdf - Published Version
Restricted to Registered users only
Download (12kB) | Request a copy
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 (Paper) |
|---|---|
| Uncontrolled Keywords: | Neural networks; Recursive functions; Technical presentations; Levenberg-marquardt; Multilayer perceptions; Novel applications; Parameter corrections; Recursive neural networks; Adaptive algorithms |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Departments / MOR / COE: | Departments > Electrical & Electronic Engineering |
| Depositing User: | Dr Vijanth Sagayan Asirvadam |
| Date Deposited: | 02 Mar 2010 01:18 |
| Last Modified: | 19 Jan 2017 08:26 |
| URI: | http://scholars.utp.edu.my/id/eprint/259 |
Actions (login required)
- View Item