Abstract
Hardware Implementation of Neural Network Training with Levenberg-Marquardt AlgorithmLevenberg-Marquardt (LM) algorithm is preferred due to providing fast convergence and stability in training of artificial neural networks (ANN). In this study, hardware implementation of ANN training with LM algorithm is presented on FPGA using floating point number representation..Hardware implementation has been realized on Virtex-5 xc5vlx110-3ff1153 FPGA using ISE Webpack 10.1 software. In this work, both ANN and its training using LM have been particularly implemented on FPGA according to the inherent parallel data processing of ANN. Obtained synthesis results have showed that training of ANN using LM algorithm can be successfully implemented on FPGA.
Keywords
Abstract
Levenberg-Marquardt (LM) algoritması yapay sinir ağlarının eğitiminde sağlamış olduğu hız ve kararlılık nedeni ile tercih edilmektedir. Bu çalışmada yapay sinir ağı (YSA) eğitiminin LM algoritması ile kayan noktalı sayı formatında donanımsal olarak FPGA’da gerçeklenmesi sunulmuştur. Donanımsal gerçekleme ISE Webpack10.1 programı kullanılarak Xilinx Virtex 5 xc5vlx110-3ff1153 FPGA’sı üzerinde gerçeklenmiştir. Çalışmada özellikle YSA mimarisinin doğasında var olan paralelliğin FPGA üzerine aktarılmasının yanı sıra eğitim aşamasında LM algoritması da paralel veri işlemeye uygun olarak gerçeklenmiştir. Elde edilen sentez sonuçları, LM ile YSA eğitiminin FPGA üzerinde başarı ile gerçeklenebileceğini ortaya koymuştur.
Keywords
Details
| Other ID | JA37JM69AN |
|---|---|
| Journal Section | Makaleler(Araştırma) |
| Authors | |
| Publication Date | June 24, 2016 |
| Published in Issue | Year 2012 Volume: 5 Issue: 1 |
Cite
Article Acceptance
Use user registration/login to upload articles online.
The acceptance process of the articles sent to the journal consists of the following stages:
1. Each submitted article is sent to at least two referees at the first stage.
2. Referee appointments are made by the journal editors. There are approximately 200 referees in the referee pool of the journal and these referees are classified according to their areas of interest. Each referee is sent an article on the subject he is interested in. The selection of the arbitrator is done in a way that does not cause any conflict of interest.
3. In the articles sent to the referees, the names of the authors are closed.
4. Referees are explained how to evaluate an article and are asked to fill in the evaluation form shown below.
5. The articles in which two referees give positive opinion are subjected to similarity review by the editors. The similarity in the articles is expected to be less than 25%.
6. A paper that has passed all stages is reviewed by the editor in terms of language and presentation, and necessary corrections and improvements are made. If necessary, the authors are notified of the situation.
.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.