计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (5): 281-290.doi: 10.11896/jsjkx.240100017

• 人工智能 • 上一篇    下一篇

动态增量式逆向云变换算法研究

许昌林1,2, 孔令卓1   

  1. 1 北方民族大学数学与信息科学学院 银川 750021
    2 北方民族大学宁夏智能信息与大数据处理重点实验室 银川 750021
  • 收稿日期:2024-01-02 修回日期:2024-05-21 出版日期:2025-05-15 发布日期:2025-05-12
  • 通讯作者: 许昌林(xuchlin@163.com)
  • 基金资助:
    宁夏自然科学基金优秀青年项目(2023AAC05046);国家自然科学基金(62066001);宁夏高等教育一流学科建设基金(NXYLXK2017B09)

Research on Dynamic Incremental Backward Cloud Transformation Algorithm

XU Changlin1,2, KONG Lingzhuo1   

  1. 1 School of Mathematics and Information Science,North Minzu University,Yinchuan 750021,China
    2 The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province,North Minzu University,Yinchuan 750021,China
  • Received:2024-01-02 Revised:2024-05-21 Online:2025-05-15 Published:2025-05-12
  • About author:XU Changlin,born in 1983,Ph.D,associate professor,master's supervisor,is a member of CCF(No.R4034M).His main research interests include intelligent information processing,cloud model theory,cognitive computing and uncertainty decision-making.
  • Supported by:
    Excellent Youth Program of Ningxia Natural Science Foundation(2023AAC05046),National Natural Science Foundation of China(62066001) and Ningxia Higher Education First-class Discipline Construction Project(NXYLXK2017B09).

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摘要： 云模型作为研究不确定性信息的工具,在不确定性人工智能和数据挖掘方面具有重要意义。 其中逆向云变换算法为云模型的重要算法之一,可以实现定量数据到定性概念的转换。文中从动态增量的角度对逆向云变换算法进行研究。首先,针对现有的基于一阶绝对中心矩的经典逆向云变换算法中参数估计存在的不合理性进行了理论分析。其次,在理论分析的基础上,结合正向云变换算法生成云滴的特点,利用正态随机变量动态产生新的云滴作为新增样本,并将随机生成的样本和原有样本融合作为最终样本后再对参数进行估计,有效解决了已有算法存在的估算问题,从而提出了两种动态增量式的逆向云变换算法。然后,通过随机模拟实验,从有效性、稳定性、收敛性和参数的鲁棒性4个方面将所提出的逆向云变换算法与已有算法进行对比分析,实验结果表明所提出的动态增量式逆向云变换算法的估计误差较小、稳定性和收敛性较好,且对参数的变化具有较强的鲁棒性。最后,将提出的逆向云变换算法应用在对射击选手的射击水平模拟还原和评价中,实验结果进一步表明算法具有较好的实用性。

关键词: 云模型, 逆向云变换, 云滴, 动态增量, 超熵

Abstract: As a tool for studying uncertain information,cloud model is of great significance in uncertain artificial intelligence and data mining.The backward cloud transformation algorithm is one of the important algorithms of cloud model,which can realize the transformation from quantitative data to qualitative concepts.This paper mainly studies the backward cloud transformation algorithm from the perspective of dynamic increment.Firstly,the irrationality of parameter estimation in the existing classical backward cloud transformation algorithm based on the first-order absolute central moment is analyzed theoretically.Secondly,on the basis of theoretical analysis,combined with the characteristics of cloud droplets generated by the forward cloud transformation algorithm,the normal random variable is used to dynamically generate new cloud droplets as new samples,then the randomly generated samples and the original samples are fused as the final samples to estimate the parameters,which effectively solves the estimation problems existing in the existing algorithms.Therefore,two dynamic incremental backward cloud transformation algorithms are proposed.Thirdly,through random simulation experiments,this paper compares the proposed backward cloud transform algorithm with existing algorithms from four aspects:effectiveness,stability,convergence and parameter robustness.The experimental results show that the dynamic incremental backward cloud transformation algorithm proposed in this paper has smaller estimation error,better stability and convergence,and has strong robustness to parameter changes.Finally,the proposed backward cloud transform algorithm is applied to the simulation and evaluation of Shooters' shooting level.The experimental results further show that the proposed algorithms have preferably practicability.

Key words: Cloud model, Backward cloud transformation, Cloud drop, Dynamic increment, Hyper entropy

中图分类号: 

  • TP18
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