Hysteresis Neural Networks for N-Queens Problems
Summary :
We propose a hysteresis neural network system solving NP-Hard optimization problems, the N-Queens Problem. The continuous system with binary outputs searches a solution of the problem without energy function. The output vector corresponds to a complete solution when the output vector becomes stable. That is, this system does never become stable without satisfying the constraints of the problem. Though it is very hard to remove limit cycle completely from this system, we can propose a new method to reduce the possibility of limit cycle by controlling time constants.
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- IEICE TRANSACTIONS on Fundamentals Vol.E82-A No.9 pp.1851-1859
- Publication Date
- 1999/09/25
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- Type of Manuscript
- Special Section PAPER (Special Section on Nonlinear Theory and Its Applications)
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Toshiya NAKAGUCHI, Kenya JIN'NO, Mamoru TANAKA, "Hysteresis Neural Networks for N-Queens Problems" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 9, pp. 1851-1859, September 1999, doi: .
Abstract: We propose a hysteresis neural network system solving NP-Hard optimization problems, the N-Queens Problem. The continuous system with binary outputs searches a solution of the problem without energy function. The output vector corresponds to a complete solution when the output vector becomes stable. That is, this system does never become stable without satisfying the constraints of the problem. Though it is very hard to remove limit cycle completely from this system, we can propose a new method to reduce the possibility of limit cycle by controlling time constants.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_9_1851/_p
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@ARTICLE{e82-a_9_1851,
author={Toshiya NAKAGUCHI, Kenya JIN'NO, Mamoru TANAKA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Hysteresis Neural Networks for N-Queens Problems},
year={1999},
volume={E82-A},
number={9},
pages={1851-1859},
abstract={We propose a hysteresis neural network system solving NP-Hard optimization problems, the N-Queens Problem. The continuous system with binary outputs searches a solution of the problem without energy function. The output vector corresponds to a complete solution when the output vector becomes stable. That is, this system does never become stable without satisfying the constraints of the problem. Though it is very hard to remove limit cycle completely from this system, we can propose a new method to reduce the possibility of limit cycle by controlling time constants.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Hysteresis Neural Networks for N-Queens Problems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1851
EP - 1859
AU - Toshiya NAKAGUCHI
AU - Kenya JIN'NO
AU - Mamoru TANAKA
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 1999
AB - We propose a hysteresis neural network system solving NP-Hard optimization problems, the N-Queens Problem. The continuous system with binary outputs searches a solution of the problem without energy function. The output vector corresponds to a complete solution when the output vector becomes stable. That is, this system does never become stable without satisfying the constraints of the problem. Though it is very hard to remove limit cycle completely from this system, we can propose a new method to reduce the possibility of limit cycle by controlling time constants.
ER -
English
