Solving multiprocessor real-time system scheduling with enhanced competitive scheme

Ruey Maw Chen, Shih Tang Lo, Yueh Min Huang

研究成果: Conference contribution

摘要

A new method based on Hopfield Neural Networks (HNN) for solving real-time scheduling problem is adopted in this study. Neural network using competitive learning rule provides a highly effective method and deriving a sound solution for scheduling problem. Moreover, competitive scheme reduces network complexity. However, competitive scheme is a 1-out-of-N confine rule and applicable for limited scheduling problems. Restated, the processor may not be full utilization for scheduling problems. To facilitate the non-fully utilized problem, extra neurons are introduced to the Competitive Hopfield Neural Network (CHNN). Slack neurons are imposed on CHNN with respected to pseudo processes. Simulation results reveal that the competitive neural network imposed on the proposed energy function with slack neurons integrated ensures an appropriate approach of solving both full and non-full utilization multiprocessor real-time system scheduling problems.

原文English
主出版物標題Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
發行者Springer Verlag
頁面1108-1117
頁數10
ISBN(列印)3540464816, 9783540464815
DOIs
出版狀態Published - 2006 一月 1
事件13th International Conference on Neural Information Processing, ICONIP 2006 - Hong Kong, China
持續時間: 2006 十月 32006 十月 6

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
4233 LNCS - II
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Other

Other13th International Conference on Neural Information Processing, ICONIP 2006
國家China
城市Hong Kong
期間06-10-0306-10-06

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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