摘要
熵作为度量序列混乱程度的特征参数,已被广泛应用于不同领域.运用仿真信号对信息熵、近似熵和模糊熵进行了全面的分析对比,验证了模糊熵的优势;提出了改进的经验模态分解方法,将算法与模糊熵结合,求出加权模糊熵.实验结果表明相比于原始信号的模糊熵,新模糊熵对一时间序列混乱程度的区分度更加明显,其值也更加稳定.
As a characteristic parameter, the entropy was used in many different areas for measuring the disordered sequence. The emulation signal was used to make a comprehensive analysis and comparison of the information entropy, approximate entropy and fuzzy entropy, which demonstrated the advantages of the fuzzy entropy. Moreover, the improved empirical mode decomposition method was proposed during the research, which combined the arithmetic with fuzzy and figured out the weighted fuzzy entropy. The experimental results showed that the new fuzzy entropy was more obvious and stable than that of the traditional method for distinguishing the babelism of the same time sequence.
作者
于本成
丁世飞
YU Bencheng;DING Shitei(School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116,China;School of Information and Electrical Engineering,Xuzhou College of Industrial Technology,Xuzhou 221004,China)
出处
《郑州大学学报（理学版）》
CAS
北大核心
2018年第4期39-44,49,共7页
Journal of Zhengzhou University:Natural Science Edition
基金
国家自然科学基金项目(61379101)
江苏省高等职业院校教师专业带头人高端研修项目(2017TDFX003)
关键词
信息熵
近似熵
模糊熵
经验模态分解
加权模糊熵
information entropy
approximate entropy
fuzzy entropy
empirical mode decomposition
weighted fuzzy entropy
