Rough-winner-take-all self-organizing neural network for hardware oriented vector quantization algorithm
Hakaru Tamukoh, Takanori Koga, Keiichi Horio, Takeshi Yamakawa · Conference proceedings · 2007
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.