DocumentCode
3266828
Title
Rough-winner-take-all self-organizing neural network for hardware oriented vector quantization algorithm
Author
Tamukoh, Hakaru ; Koga, Takanori ; Horio, Keiichi ; Yamakawa, Takeshi
Author_Institution
Kyushu Inst. of Technol., Kitakyushu
fYear
2007
fDate
5-8 Aug. 2007
Firstpage
349
Lastpage
352
Abstract
In this paper, we propose a new vector quantization method for an efficient digital hardware implementation. The basic algorithm of the proposed method is similar to K-means clustering which is the simplest vector quantization. The only different point is that the proposed method employs a rough-winner-take-all as the substitute of ordinary winner-take-all. The simulation results show that quantization performance of the proposed method is nearly equal to neural gas which is an excellent vector quantization. Besides, the proposed method features low hardware complexity as compared to neural gas.
Keywords
computational complexity; self-organising feature maps; vector quantisation; K-means clustering; digital hardware implementation; hardware complexity; hardware oriented vector quantization algorithm; rough-winner-take-all self-organizing neural network; Neural network hardware; Neural networks; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 2007. MWSCAS 2007. 50th Midwest Symposium on
Conference_Location
Montreal, Que.
ISSN
1548-3746
Print_ISBN
978-1-4244-1175-7
Electronic_ISBN
1548-3746
Type
conf
DOI
10.1109/MWSCAS.2007.4488604
Filename
4488604
Link To Document