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模糊熵:公理化定义和神经网络模型

机译:模糊熵:公理化定义和神经网络模型

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摘要

The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy system is known as fuzzy entropy. Fuzzy entropy has been focused and studied by many researchers in various fields. In this paper, firstly,the axiomatic definition of fuzzy entropy is discussed. Then, neural networks model of fuzzy entropy is proposed, based on the computing capability of neural networks. In the end, two examples are discussed to show the efficiency of the model.
机译:不确定性的衡量标准作为信息衡量标准。模糊性措施被称为模糊信息措施。从模糊集或模糊系统中获得的模糊信息的量度被称为模糊熵。模糊熵由各个领域的许多研究人员专注并研究。在本文中,讨论了模糊熵的公理定义。然后,基于神经网络的计算能力,提出了模糊熵的神经网络模型。最后,讨论了两个示例以显示模型的效率。

著录项

  • 来源
    《数学季刊(英文版)》 |2004年第3期|319-323|共5页
  • 作者

    卿铭; 曹悦; 黄天民;

  • 作者单位

    Department of Mathematics, Southwest Jiaotong University, Chengdu 610031, China;

    Zhengzhou Teacher's College, Zhengzhou 450044, China;

    Department of Mathematics, Southwest Jiaotong University, Chengdu 610031, China;

  • 收录信息 北京大学中文核心期刊目录(北大核心);中国科学引文数据库(CSCD);中国科技论文与引文数据库(CSTPCD);
  • 原文格式 PDF
  • 正文语种 chi
  • 中图分类 模式识别理论;
  • 关键词

    neural networks; BP networks; fuzzy entropy; fuzzy set; model;

  • 入库时间 2022-08-19 04:11:55

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