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
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;
Conference_Titel :
Circuits and Systems, 2007. MWSCAS 2007. 50th Midwest Symposium on
Conference_Location :
Montreal, Que.
Print_ISBN :
978-1-4244-1175-7
Electronic_ISBN :
1548-3746
DOI :
10.1109/MWSCAS.2007.4488604