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In this paper, a discrete-time convergence theorem for continuous-state Hopfield networks with self-interaction neurons is proposed. This theorem differs from the previous work by Wang in that the original updating rule is maintained while the network is still guaranteed to monotonically decrease to a stable state. The relationship between the parameters in a typical class of energy functions is also investigated, and consequently a "guided trial-and-error" technique is proposed to determine the parameter values. The third problem discussed in this paper is the post-processing of outputs, which turns out to be rather important even though it never attracts enough attention. The effectiveness of all the theorems and post-processing methods proposed in this paper is demonstrated by a large number of computer simulations on the assignment problem and the N-queen problem of different sizes.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.12 pp.3162-3173

- Publication Date
- 2001/12/01

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- PAPER

- Category
- Neural Networks and Bioengineering

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Gang FENG, Christos DOULIGERIS, "On the Convergence and Parameter Relation of Discrete-Time Continuous-State Hopfield Networks with Self-Interaction Neurons" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 12, pp. 3162-3173, December 2001, doi: .

Abstract: In this paper, a discrete-time convergence theorem for continuous-state Hopfield networks with self-interaction neurons is proposed. This theorem differs from the previous work by Wang in that the original updating rule is maintained while the network is still guaranteed to monotonically decrease to a stable state. The relationship between the parameters in a typical class of energy functions is also investigated, and consequently a "guided trial-and-error" technique is proposed to determine the parameter values. The third problem discussed in this paper is the post-processing of outputs, which turns out to be rather important even though it never attracts enough attention. The effectiveness of all the theorems and post-processing methods proposed in this paper is demonstrated by a large number of computer simulations on the assignment problem and the N-queen problem of different sizes.

URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_12_3162/_p

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@ARTICLE{e84-a_12_3162,

author={Gang FENG, Christos DOULIGERIS, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={On the Convergence and Parameter Relation of Discrete-Time Continuous-State Hopfield Networks with Self-Interaction Neurons},

year={2001},

volume={E84-A},

number={12},

pages={3162-3173},

abstract={In this paper, a discrete-time convergence theorem for continuous-state Hopfield networks with self-interaction neurons is proposed. This theorem differs from the previous work by Wang in that the original updating rule is maintained while the network is still guaranteed to monotonically decrease to a stable state. The relationship between the parameters in a typical class of energy functions is also investigated, and consequently a "guided trial-and-error" technique is proposed to determine the parameter values. The third problem discussed in this paper is the post-processing of outputs, which turns out to be rather important even though it never attracts enough attention. The effectiveness of all the theorems and post-processing methods proposed in this paper is demonstrated by a large number of computer simulations on the assignment problem and the N-queen problem of different sizes.},

keywords={},

doi={},

ISSN={},

month={December},}

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TY - JOUR

TI - On the Convergence and Parameter Relation of Discrete-Time Continuous-State Hopfield Networks with Self-Interaction Neurons

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 3162

EP - 3173

AU - Gang FENG

AU - Christos DOULIGERIS

PY - 2001

DO -

JO - IEICE TRANSACTIONS on Fundamentals

SN -

VL - E84-A

IS - 12

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - December 2001

AB - In this paper, a discrete-time convergence theorem for continuous-state Hopfield networks with self-interaction neurons is proposed. This theorem differs from the previous work by Wang in that the original updating rule is maintained while the network is still guaranteed to monotonically decrease to a stable state. The relationship between the parameters in a typical class of energy functions is also investigated, and consequently a "guided trial-and-error" technique is proposed to determine the parameter values. The third problem discussed in this paper is the post-processing of outputs, which turns out to be rather important even though it never attracts enough attention. The effectiveness of all the theorems and post-processing methods proposed in this paper is demonstrated by a large number of computer simulations on the assignment problem and the N-queen problem of different sizes.

ER -