• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Reliability Engineering & System Safety >Bayesian entropy network for fusion of different types of information
【24h】

Bayesian entropy network for fusion of different types of information

机译：贝叶斯熵网络用于融合不同类型的信息

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A hybrid method for information fusion combining the maximum entropy (ME) method with the classical Bayesian network is proposed as the Bayesian-Entropy Network (BEN) in this paper. The key benefit of the proposed method is the capability to handle various types of information for classification and updating, such as classical point data, abstracted statistical information, and range data. The detailed derivation of the proposed is given and special focus is on the formulation of different types of information as constraints embedded in the entropy part. The Bayesian part is used to handle classical point observation data. Next, an adaptive algorithm is proposed to mitigate the impact of wrong information constraints on the final posterior distribution estimation. Following this, several examples are used to demonstrate the proposed methodology and application to engineering problems. It is shown that the proposed method is a generalized form of classical Bayesian method, and can take advantage of the extra information. This advantage is preferable in many engineering applications especially when the number of point observations is limited. Conclusions and future work are drawn based on the current study.
机译：本文提出一种将最大熵（ME）方法与经典贝叶斯网络相结合的信息融合混合方法，作为贝叶斯熵网络（BEN）。提出的方法的主要好处是能够处理各种类型的信息以进行分类和更新，例如经典点数据，抽象统计信息和范围数据。给出了该提议的详细推导，并且特别着重于将不同类型的信息的公式化作为约束嵌入在熵部分中。贝叶斯部分用于处理经典点观测数据。接下来，提出了一种自适应算法来减轻错误信息约束对最终后验分布估计的影响。在此之后，将使用几个示例来演示所提出的方法论及其在工程问题中的应用。结果表明，所提出的方法是经典贝叶斯方法的一种广义形式，可以利用额外的信息。在许多工程应用中，尤其是在点观测数量有限的情况下，此优点是可取的。根据当前的研究得出结论和未来的工作。

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