A Dynamical N-Queen Problem Solver Using Hysteresis Neural Networks
Summary :
In a previous study about a combinatorial optimization problem solver using neural networks, since the Hopfield method, convergence to the optimum solution sooner and with more certainty is regarded as important. Namely, only static states are considered as the information. However, from a biological point of view, dynamical systems have attracted attention recently. Therefore, we propose a "dynamical" combinatorial optimization problem solver using hysteresis neural networks. In this paper, the proposed system is evaluated by the N-Queen problem.
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- IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.4 pp.740-745
- Publication Date
- 2003/04/01
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- Type of Manuscript
- Special Section PAPER (Special Section of Selected Papers from the 15th Workshop on Circuits and Systems in Karuizawa)
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Takao YAMAMOTO, Kenya JIN'NO, Haruo HIROSE, "A Dynamical N-Queen Problem Solver Using Hysteresis Neural Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E86-A, no. 4, pp. 740-745, April 2003, doi: .
Abstract: In a previous study about a combinatorial optimization problem solver using neural networks, since the Hopfield method, convergence to the optimum solution sooner and with more certainty is regarded as important. Namely, only static states are considered as the information. However, from a biological point of view, dynamical systems have attracted attention recently. Therefore, we propose a "dynamical" combinatorial optimization problem solver using hysteresis neural networks. In this paper, the proposed system is evaluated by the N-Queen problem.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e86-a_4_740/_p
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@ARTICLE{e86-a_4_740,
author={Takao YAMAMOTO, Kenya JIN'NO, Haruo HIROSE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Dynamical N-Queen Problem Solver Using Hysteresis Neural Networks},
year={2003},
volume={E86-A},
number={4},
pages={740-745},
abstract={In a previous study about a combinatorial optimization problem solver using neural networks, since the Hopfield method, convergence to the optimum solution sooner and with more certainty is regarded as important. Namely, only static states are considered as the information. However, from a biological point of view, dynamical systems have attracted attention recently. Therefore, we propose a "dynamical" combinatorial optimization problem solver using hysteresis neural networks. In this paper, the proposed system is evaluated by the N-Queen problem.},
keywords={},
doi={},
ISSN={},
month={April},}
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TY - JOUR
TI - A Dynamical N-Queen Problem Solver Using Hysteresis Neural Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 740
EP - 745
AU - Takao YAMAMOTO
AU - Kenya JIN'NO
AU - Haruo HIROSE
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2003
AB - In a previous study about a combinatorial optimization problem solver using neural networks, since the Hopfield method, convergence to the optimum solution sooner and with more certainty is regarded as important. Namely, only static states are considered as the information. However, from a biological point of view, dynamical systems have attracted attention recently. Therefore, we propose a "dynamical" combinatorial optimization problem solver using hysteresis neural networks. In this paper, the proposed system is evaluated by the N-Queen problem.
ER -
English
