A Data Peprocessing Technique Based on Information Entropy

RUAN Xiang-chao; WAN Ding-sheng

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A Data Peprocessing Technique Based on Information Entropy

更新时间：2026-09-04

- A Data Peprocessing Technique Based on Information Entropy
- Vol. 35, Issue 2, Pages: 104-108(2018)
- 作者机构：
  
  河海大学计算机与信息学院,江苏,南京,210098
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2018
- 稿件说明：
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阮祥超, 万定生. 一种基于信息熵的数据预处理技术[J]. 微电子学与计算机, 2018, 35(2): 104-108.

RUAN Xiang-chao, WAN Ding-sheng. A Data Peprocessing Technique Based on Information Entropy[J]. 2018, 35(2): 104-108.
阮祥超, 万定生. 一种基于信息熵的数据预处理技术[J]. 微电子学与计算机, 2018, 35(2): 104-108. DOI：

RUAN Xiang-chao, WAN Ding-sheng. A Data Peprocessing Technique Based on Information Entropy[J]. 2018, 35(2): 104-108. DOI：

摘要

神经网络是数据挖掘领域中的重要研究课题.针对水文数据不规则和非线性的数据特征

提出了一种基于信息熵的数据预处理方法

使用了基于改进的BP神经网络模型.该模型采用计算范围熵的熵值的方法

遍历查找替换输入变量

使得输入数据集的变量间线性关系减弱

非线性关系增加

熵值增大

达到提高水文预测精度的目的.在实际的预测中基于相关信息熵的改进神经网络模型的预测效果优于传统预测模型.

Abstract

Neural network is an important research topic in the field of data mining. This paper proposing a data preprocessing method based on information entropy which is directed to the irregular and non-linear data characteristics of hydrological data

and uses an improved model of BP neural network. The model uses the method of calculating the entropy of the range entropy

traversing the input variables

searching and replacing them

making the linear relationship between the variables of the input data set weakened. By this way

the non-linear relationship increasing

and the value of the information entropy increasing

so that it can achieving the purpose of improving the accuracy of hydrological prediction. In the actual prediction

the improved model of neural based on the relevant information entropy is superior to the traditional prediction model.

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references

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张继学, 王鹏, 张琳, 等. 人工神经网络在短期降水预测方面的应用研究[J]. 科技风, 2016, 28(17): 123-124. DOI: 123

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