The Scheduling of the Parameters in Hopfield Neural Networks with Fuzzy Control
Summary :
In this paper, we proposes a novel fuzzy control for parameter scheduling of the Hopfield neural network. When a combinatorial optimization problem, such as the traveling salesman problem, is solved by Hopfield neural network, it is efficient to adaptively change the parameters of the energy function and sigmoid function. By changing the parameters on purpose, this network can avoid being trapped at a local minima. Since there exists complex relations among these parameters, it is difficult to analytically determine the ideal scheduling. First, we investigate a bad scheduling to change parameters by simple experiments and find several rules that may lead to a good scheduling. The rules extracted from the experimental results are then realized by fuzzy control. By using fuzzy control, we can judge bad scheduling from vague network stages, and then correct the relations among the parameters. Computer simulation results of the Traveling Salesman Problem (TSP) is considered as an example to demonstrate its validity.
- Publication
- IEICE TRANSACTIONS on Information Vol.E77-D No.8 pp.895-903
- Publication Date
- 1994/08/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence and Cognitive Science
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Tomoyuki UEDA, Kiyoshi TAKAHASHI, Chun-Ying HO, Shinsaku MORI, "The Scheduling of the Parameters in Hopfield Neural Networks with Fuzzy Control" in IEICE TRANSACTIONS on Information,
vol. E77-D, no. 8, pp. 895-903, August 1994, doi: .
Abstract: In this paper, we proposes a novel fuzzy control for parameter scheduling of the Hopfield neural network. When a combinatorial optimization problem, such as the traveling salesman problem, is solved by Hopfield neural network, it is efficient to adaptively change the parameters of the energy function and sigmoid function. By changing the parameters on purpose, this network can avoid being trapped at a local minima. Since there exists complex relations among these parameters, it is difficult to analytically determine the ideal scheduling. First, we investigate a bad scheduling to change parameters by simple experiments and find several rules that may lead to a good scheduling. The rules extracted from the experimental results are then realized by fuzzy control. By using fuzzy control, we can judge bad scheduling from vague network stages, and then correct the relations among the parameters. Computer simulation results of the Traveling Salesman Problem (TSP) is considered as an example to demonstrate its validity.
URL: https://global.ieice.org/en_transactions/information/10.1587/e77-d_8_895/_p
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@ARTICLE{e77-d_8_895,
author={Tomoyuki UEDA, Kiyoshi TAKAHASHI, Chun-Ying HO, Shinsaku MORI, },
journal={IEICE TRANSACTIONS on Information},
title={The Scheduling of the Parameters in Hopfield Neural Networks with Fuzzy Control},
year={1994},
volume={E77-D},
number={8},
pages={895-903},
abstract={In this paper, we proposes a novel fuzzy control for parameter scheduling of the Hopfield neural network. When a combinatorial optimization problem, such as the traveling salesman problem, is solved by Hopfield neural network, it is efficient to adaptively change the parameters of the energy function and sigmoid function. By changing the parameters on purpose, this network can avoid being trapped at a local minima. Since there exists complex relations among these parameters, it is difficult to analytically determine the ideal scheduling. First, we investigate a bad scheduling to change parameters by simple experiments and find several rules that may lead to a good scheduling. The rules extracted from the experimental results are then realized by fuzzy control. By using fuzzy control, we can judge bad scheduling from vague network stages, and then correct the relations among the parameters. Computer simulation results of the Traveling Salesman Problem (TSP) is considered as an example to demonstrate its validity.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - The Scheduling of the Parameters in Hopfield Neural Networks with Fuzzy Control
T2 - IEICE TRANSACTIONS on Information
SP - 895
EP - 903
AU - Tomoyuki UEDA
AU - Kiyoshi TAKAHASHI
AU - Chun-Ying HO
AU - Shinsaku MORI
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E77-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 1994
AB - In this paper, we proposes a novel fuzzy control for parameter scheduling of the Hopfield neural network. When a combinatorial optimization problem, such as the traveling salesman problem, is solved by Hopfield neural network, it is efficient to adaptively change the parameters of the energy function and sigmoid function. By changing the parameters on purpose, this network can avoid being trapped at a local minima. Since there exists complex relations among these parameters, it is difficult to analytically determine the ideal scheduling. First, we investigate a bad scheduling to change parameters by simple experiments and find several rules that may lead to a good scheduling. The rules extracted from the experimental results are then realized by fuzzy control. By using fuzzy control, we can judge bad scheduling from vague network stages, and then correct the relations among the parameters. Computer simulation results of the Traveling Salesman Problem (TSP) is considered as an example to demonstrate its validity.
ER -
English
