摘要
针对传统双冗余网络高维离散数据特征检测方法存在检测精度较低,数据扰动扰动问题,提出基于模糊参数融合的双冗余网络高维离散数据特征检测方法.首先,构建双冗余网络高维数据分布结构模型,实现高维离散数据的融合处理;其次,对高维离散数据的统计特征分析;最后,通过模糊度检测实现高维离散数据特征检测过程中的自适应寻优和收敛性控制.仿真结果表明,设计的特征检测法准确性较高,特征辨识度较好.设计方法提高了双冗余网络数据传输和访问调度性能.
Aiming at the problems of traditional dual-redundant network high-dimensional discrete data feature detection methods with low detection accuracy and data disturbance,a dual-redundant network high-dimensional discrete data feature detection method based on fuzzy parameter fusion is proposed.First,a dual-redundant network high-dimensional data distribution structure model to realize the fusion processing of high-dimensional discrete data,Secondly,analysis of statistical characteristics of high-dimensional discrete data,Finally,the ambiguity detection is used to realize the adaptive optimization and convergence control in the process of high-dimensional discrete data feature detection.The simulation results show that the designed feature detection method has high accuracy and better feature recognition.The design method improves the performance of dualredundant network data transmission and access scheduling.
作者
肖峰
XIAO Feng(School of Information Engineering,Anhui Vocational and Technical College,Hefei Anhui 230011)
出处
《宁夏师范学院学报》
2021年第1期67-72,共6页
Journal of Ningxia Normal University
基金
安徽省教育厅自然科学基金项目(KJ2017A477).
关键词
双冗余网络
高维离散数据
特征检测
云融合
自适应回归分析
模糊度分析
Dual redundant network
High-dimensional discrete data
Feature detection
Cloud convergence
Adaptive regression analysis
Ambiguity analysis
