...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Procedia Manufacturing >A Decision Trees-based knowledge mining approach for controlling a complex production system
【24h】

A Decision Trees-based knowledge mining approach for controlling a complex production system

机译：一种基于决策树的知识挖掘方法，用于控制复杂的生产系统

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this study, we use decision trees constructed by learning-with-supervision techniques for representing the best policies found by a Reinforcement Learning algorithm applied to an optimization problem of a complex production system. Until now, the relative scientific literature includes studies that mainly propose dynamic programming-based approaches for treating such kind of combinatorial problems. Decision trees are used to approximate functions of multiple variables with discrete values. In this case the “leaves” of the tree correspond to the set of function values while the non-terminal nodes to its independent variables. In the present research, the parameters of the optimization problem and the corresponding optimal policies found by the Reinforcement Learning algorithm applied, will be used as the training data set. Representing the best found policies using decision trees will support more effective qualitative analysis and further understanding of their properties.
机译：在这项研究中，我们使用通过学习的监督技术构成的决策树来表示应用于复杂生产系统的优化问题的加强学习算法所发现的最佳策略。到目前为止，相对科学文献包括主要提出基于动态编程的方法来治疗这种组合问题的研究。决策树用于以离散值近似多变量的近似。在这种情况下，树的“叶子”对应于非终端节点到其独立变量的函数值集。在本研究中，优化问题的参数和应用的加强学习算法发现的相应最佳政策将用作训练数据集。代表使用决策树的最佳发现政策将支持更有效的定性分析和对其性质的进一步了解。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号