Abstract
This paper presents an N2 neuron N-parallel computation method for a neural network for the N queens problem. In this method, the N2 neurons are partitioned into N groups of N neurons, where the states of N neurons in N different groups are updated synchronously and the states in the same group are updated sequentially. First, we compare the performance of two existing neural networks for the N queens problem in order to show that Takefuji's neural network is best in the sequential method. Then, we show that even the improved neural network using the N2-parallel method cannot provide the same performance as that using the using sequential method. Lastly, we propose an N-parallel method to achieve high performance and parallel computation simultaneously.
| Original language | English |
|---|---|
| Pages (from-to) | 12-19 |
| Number of pages | 8 |
| Journal | Electronics and Communications in Japan, Part III: Fundamental Electronic Science (English translation of Denshi Tsushin Gakkai Ronbunshi) |
| Volume | 80 |
| Issue number | 11 |
| Publication status | Published - Nov 1997 |
| Externally published | Yes |
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Keywords
- N-parallel computation
- N-queens problem
- Neural network
ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
Proposal of an N-parallel computation method for a neural network for the N queens problem. / Yoshio, Hiroaki; Baba, Takayuki; Funabiki, Nobuo; Nishikawa, Seishi.
In: Electronics and Communications in Japan, Part III: Fundamental Electronic Science (English translation of Denshi Tsushin Gakkai Ronbunshi), Vol. 80, No. 11, 11.1997, p. 12-19.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Proposal of an N-parallel computation method for a neural network for the N queens problem
AU - Yoshio, Hiroaki
AU - Baba, Takayuki
AU - Funabiki, Nobuo
AU - Nishikawa, Seishi
PY - 1997/11
Y1 - 1997/11
N2 - This paper presents an N2 neuron N-parallel computation method for a neural network for the N queens problem. In this method, the N2 neurons are partitioned into N groups of N neurons, where the states of N neurons in N different groups are updated synchronously and the states in the same group are updated sequentially. First, we compare the performance of two existing neural networks for the N queens problem in order to show that Takefuji's neural network is best in the sequential method. Then, we show that even the improved neural network using the N2-parallel method cannot provide the same performance as that using the using sequential method. Lastly, we propose an N-parallel method to achieve high performance and parallel computation simultaneously.
AB - This paper presents an N2 neuron N-parallel computation method for a neural network for the N queens problem. In this method, the N2 neurons are partitioned into N groups of N neurons, where the states of N neurons in N different groups are updated synchronously and the states in the same group are updated sequentially. First, we compare the performance of two existing neural networks for the N queens problem in order to show that Takefuji's neural network is best in the sequential method. Then, we show that even the improved neural network using the N2-parallel method cannot provide the same performance as that using the using sequential method. Lastly, we propose an N-parallel method to achieve high performance and parallel computation simultaneously.
KW - N-parallel computation
KW - N-queens problem
KW - Neural network
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UR - http://www.scopus.com/inward/citedby.url?scp=0031271838&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0031271838
VL - 80
SP - 12
EP - 19
JO - Electronics and Communications in Japan, Part III: Fundamental Electronic Science (English translation of Denshi Tsushin Gakkai Ronbunshi)
JF - Electronics and Communications in Japan, Part III: Fundamental Electronic Science (English translation of Denshi Tsushin Gakkai Ronbunshi)
SN - 1042-0967
IS - 11
ER -