Comparisons of Seven Neural Network Models on Traffic Control Problems in Multistage Interconnection Networks

Nobuo Funabiki; Yoshiyasu Takefuji; Kuo Chun Lee

doi:10.1109/12.214695

Comparisons of Seven Neural Network Models on Traffic Control Problems in Multistage Interconnection Networks

Nobuo Funabiki, Yoshiyasu Takefuji, Kuo Chun Lee

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

Abstract

This paper presents performance comparisons of seven neural network models on traffic control problems in multistage interconnection networks. The decay term, three neuron models, and two heuristics were evaluated. The goal of the traffic control problems is to find conflict-free switching configurations with the maximum throughput. Our simulation results show that the hysteresis McCulloch-Pitts neuron model without the decay term and with two heuristics has the best performance.

Original language	English
Pages (from-to)	497-501
Number of pages	5
Journal	IEEE Transactions on Computers
Volume	42
Issue number	4
DOIs	https://doi.org/10.1109/12.214695
Publication status	Published - Apr 1993
Externally published	Yes

Keywords

Decay term
McCulloch-Pitts neuron model
hysteresis McCulloch-Pitts neuron model
multistage interconnection network
neural network
optimization
parallel algorithm
sigmoid neuron model

ASJC Scopus subject areas

Software
Theoretical Computer Science
Hardware and Architecture
Computational Theory and Mathematics

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10.1109/12.214695

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title = "Comparisons of Seven Neural Network Models on Traffic Control Problems in Multistage Interconnection Networks",

abstract = "This paper presents performance comparisons of seven neural network models on traffic control problems in multistage interconnection networks. The decay term, three neuron models, and two heuristics were evaluated. The goal of the traffic control problems is to find conflict-free switching configurations with the maximum throughput. Our simulation results show that the hysteresis McCulloch-Pitts neuron model without the decay term and with two heuristics has the best performance.",

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AB - This paper presents performance comparisons of seven neural network models on traffic control problems in multistage interconnection networks. The decay term, three neuron models, and two heuristics were evaluated. The goal of the traffic control problems is to find conflict-free switching configurations with the maximum throughput. Our simulation results show that the hysteresis McCulloch-Pitts neuron model without the decay term and with two heuristics has the best performance.

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KW - McCulloch-Pitts neuron model

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KW - optimization

KW - parallel algorithm

KW - sigmoid neuron model

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Comparisons of Seven Neural Network Models on Traffic Control Problems in Multistage Interconnection Networks

Abstract

Keywords

ASJC Scopus subject areas

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