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A missing Power data filling Method Based on Improved Random Forest Algorithm

Chinese Journal of Electrical Engineering 2019年第4期5卷 33-39页

作者： Wei Deng Yixiu Guo Jie Liu Yong Li Dingguo Liu Liang Zhu State Grid Hunan Power Company Limited Research Institute Changsha 410007China College of Electrical and Information Engineering Hunan UniversityChangsha 410082China State Grid Hunan Electric Power Company Limited Changsha 410004China

missing data filling is a key step in power big data preprocessing,which helps to improve the quality and the utilization of electric power *** to the limitations of the traditional methods of filling missing data,an improved random forest filling algorithm is *** a result of the horizontal and vertical directions of the electric power data are based on the characteristics of time ***,the method of improved random forest filling missing data combines the methods of linear interpolation,matrix combination and matrix transposition to solve the problem of filling large amount of electric power missing *** filling results show that the improved random forest filling algorithm is applicable to filling electric power data in various missing ***’s more,the accuracy of the filling results is high and the stability of the model is strong,which is beneficial in improving the quality of electric power data.

关键词： Big data cleaning missing data filling data preprocessing random forest data quality

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Target threat estimation based on discrete dynamic Bayesian networks with small samples

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Journal of Systems Engineering and Electronics 2022年第5期33卷 1135-1142页

作者： YE Fang MAO Ying LI Yibing LIU Xinrui College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information TechnologyHarbin Engineering UniversityHarbin 150001China

The accuracy of target threat estimation has a great impact on command *** Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target threat ***,the traditional discrete dynamic Bayesian network(DDBN)has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information *** the finiteness and discreteness of DDBN parameters,a fuzzy k-nearest neighbor(KNN)algorithm based on correlation of feature quantities(CF-FKNN)is proposed for DDBN parameter ***,the correlation between feature quantities is calculated,and then the KNN algorithm with fuzzy weight is introduced to fill the missing *** this basis,a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and *** results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing,and improve the effect of DDBN parameter learning in the case of serious sample *** the proposed method,the final target threat assessment results are reasonable,which meets the needs of engineering applications.

关键词： discrete dynamic Bayesian network(DDBN) parameter learning missing data filling Bayesian estimation

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A Decision-making Method Based on Soft Set under Incomplete Information

A Decision-making Method Based on Soft Set under Incomplete ...

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第25届中国控制与决策会议

作者： Danmei Zhu Yongwang Li Hong Nie Chunyu Huang School of Science Liaoning Shihua University

ISBN: (纸本)9781467355339

Incomplete information in a soft set restricts the usage of the soft set. To make the incomplete soft set more useful, in this paper, we propose an algorithm with missing data filling for incomplete soft set, in which missing data is filled in terms of the association between the parameters. Based on this, we propose a decision-making method to meet the requirements of different customers. At last, an illustrative example is provided to show the feasibility and validity of our approach in practical applications.

关键词： Soft Set missing data filling Incomplete Information Decision-making

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