Competitive neural network to solve scheduling problems

Ruey Maw Chen, Yueh Min Huang

研究成果: Article同行評審

40 引文 斯高帕斯(Scopus)

摘要

Most scheduling problems have been demonstrated to be NP-complete problems. The Hopfield neural network is commonly applied to obtain an optimal solution in various different scheduling applications, such as the traveling salesman problem (TSP), a typical discrete combinatorial problem. Hopfield neural networks, although providing rapid convergence to the solution, require extensive effort to determine coefficients. A competitive learning rule provides a highly effective means of attaining a sound solution and can reduce the effort of obtaining coefficients. Restated, the competitive mechanism reduces the network complexity. This important feature is applied to the Hopfield neural network to derive a new technique, i.e. the competitive Hopfield neural network technique. This investigation employs the competitive Hopfield neural network to resolve a multiprocessor problem with no process migration, time constraints (execution time and deadline), and limited resources. Simulation results demonstrate that the competitive Hopfield neural network imposed on the proposed energy function ensures an appropriate approach to solving this class of scheduling problems.

原文English
頁(從 - 到)177-196
頁數20
期刊Neurocomputing
37
發行號1-4
DOIs
出版狀態Published - 2001

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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