IEICE TRANSACTIONS on Information
Binary Neural Network with Negative Self-Feedback and Its Application to N-Queens Problem
Summary :
This article deals with the binary neural network with negative self-feedback connections as a method for solving combinational optimization problems. Although the binary neural network has a high convergence speed, it hardly searches out the optimum solution, because the neuron is selected randomly at each state update. In thie article, an improvement using the negative self-feedback is proposed. First it is shown that the negative self-feedback can make some local minimums be unstable. Second a selection rule is proposed and its property is analyzed in detail. In the binary neural network with negative self-feedback, this selection rule is effective to escape a local minimum. In order to comfirm the effectiveness of this selection rule, some computer simulations are carried out for the N-Queens problem. For N=256, the network is not caught in any local minimum and provides the optimum solution within 2654 steps (about 10 minutes).
- Publication
- IEICE TRANSACTIONS on Information Vol.E77-D No.4 pp.459-465
- Publication Date
- 1994/04/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Issue on Neurocomputing)
- Category
- Network Synthesis
Authors
Keyword
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Masaya OHTA, Akio OGIHARA, Kunio FUKUNAGA, "Binary Neural Network with Negative Self-Feedback and Its Application to N-Queens Problem" in IEICE TRANSACTIONS on Information,
vol. E77-D, no. 4, pp. 459-465, April 1994, doi: .
Abstract: This article deals with the binary neural network with negative self-feedback connections as a method for solving combinational optimization problems. Although the binary neural network has a high convergence speed, it hardly searches out the optimum solution, because the neuron is selected randomly at each state update. In thie article, an improvement using the negative self-feedback is proposed. First it is shown that the negative self-feedback can make some local minimums be unstable. Second a selection rule is proposed and its property is analyzed in detail. In the binary neural network with negative self-feedback, this selection rule is effective to escape a local minimum. In order to comfirm the effectiveness of this selection rule, some computer simulations are carried out for the N-Queens problem. For N=256, the network is not caught in any local minimum and provides the optimum solution within 2654 steps (about 10 minutes).
URL: https://global.ieice.org/en_transactions/information/10.1587/e77-d_4_459/_p
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@ARTICLE{e77-d_4_459,
author={Masaya OHTA, Akio OGIHARA, Kunio FUKUNAGA, },
journal={IEICE TRANSACTIONS on Information},
title={Binary Neural Network with Negative Self-Feedback and Its Application to N-Queens Problem},
year={1994},
volume={E77-D},
number={4},
pages={459-465},
abstract={This article deals with the binary neural network with negative self-feedback connections as a method for solving combinational optimization problems. Although the binary neural network has a high convergence speed, it hardly searches out the optimum solution, because the neuron is selected randomly at each state update. In thie article, an improvement using the negative self-feedback is proposed. First it is shown that the negative self-feedback can make some local minimums be unstable. Second a selection rule is proposed and its property is analyzed in detail. In the binary neural network with negative self-feedback, this selection rule is effective to escape a local minimum. In order to comfirm the effectiveness of this selection rule, some computer simulations are carried out for the N-Queens problem. For N=256, the network is not caught in any local minimum and provides the optimum solution within 2654 steps (about 10 minutes).},
keywords={},
doi={},
ISSN={},
month={April},}
Copy
TY - JOUR
TI - Binary Neural Network with Negative Self-Feedback and Its Application to N-Queens Problem
T2 - IEICE TRANSACTIONS on Information
SP - 459
EP - 465
AU - Masaya OHTA
AU - Akio OGIHARA
AU - Kunio FUKUNAGA
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E77-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 1994
AB - This article deals with the binary neural network with negative self-feedback connections as a method for solving combinational optimization problems. Although the binary neural network has a high convergence speed, it hardly searches out the optimum solution, because the neuron is selected randomly at each state update. In thie article, an improvement using the negative self-feedback is proposed. First it is shown that the negative self-feedback can make some local minimums be unstable. Second a selection rule is proposed and its property is analyzed in detail. In the binary neural network with negative self-feedback, this selection rule is effective to escape a local minimum. In order to comfirm the effectiveness of this selection rule, some computer simulations are carried out for the N-Queens problem. For N=256, the network is not caught in any local minimum and provides the optimum solution within 2654 steps (about 10 minutes).
ER -
English
