Scaling properties of neural networks for job-shop scheduling

Simon Y. Foo, Yoshiyasu Takefuji, Harold Szu

研究成果: Article査読

24 被引用数 (Scopus)

抄録

This paper investigates the scaling properties of neural networks for solving job-shop scheduling problems. Specifically, the Tank-Hopfield linear programming network is modified to solve mixed integer linear programming with the addition of step-function amplifiers. Using a linear energy function, our approach avoids the traditional problems associated with most Hopfield networks using quadratic energy functions. Although our approach requires more hardware (in terms of processing elements and resistive interconnects) than a recent approach by Zhou et al. [2], the neurons in the modified Tank-Hopfield network do not perform extensive calculations unlike those described by Zhou et al.

本文言語English
ページ(範囲)79-91
ページ数13
ジャーナルNeurocomputing
8
1
DOI
出版ステータスPublished - 1995 5月
外部発表はい

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 認知神経科学
  • 人工知能

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