Earticle

초록

영어

To solve the data missing problem caused by sensor faults during the air pollutants monitoring in henhouse, a method for missing data recovery was proposed based on support vector machine (SVM). Multiple factors that influence monitoring values of the air pollutants in henhouse, such as temporal, spatial and environmental, were considered to established a SVM regression model to estimate the missing data of the air pollutants monitoring. Meanwhile, to obtain better prediction accuracy, regression model parameters were optimized by a novel hybrid optimization algorithm which was combined standard genetic algorithm with quantum genetic strategy and simulated annealing tactics. Taking the data processing of the ammonia (NH3) concentration as an example, the proposed method was tested with the monitoring data of 3 days in a farm. The estimation results of missing data shown that there was a litter error between the estimated data and monitoring data, the maximal relative error was 5.87% (percent), the average relative error was 1.77% (percent). It is verified that this method of missing data recovery is feasible and valid.

목차

Abstract
 1. Introduction
 2. SVM Theoretical Basis
 3. Missing Data Recovery Based on QGSA-SVM
  3.1. Input-Output Parameters of SVM
  3.2. Optimization of SVM Parameters Based on QGSA
  3.3. Prediction of Missing Data by SVM
 4. Experimental Simulation and Analysis
 5. Conclusion
 Acknowledgments
 References

키워드

저자

• Jinming Liu [ College of Information Technology, Heilongjiang Bayi Agricultural University, Daqing Heilongjiang 163319, China, College of Engineering, Northeast Agricultural University, Harbin Heilongiang 150030, China ]
• Qiuju Xie [ College of Information Technology, Heilongjiang Bayi Agricultural University, Daqing Heilongjiang 163319, China ]
• Guiyang Liu [ College of Information Technology, Heilongjiang Bayi Agricultural University, Daqing Heilongjiang 163319, China ]
• Yong Sun [ College of Engineering, Northeast Agricultural University, Harbin Heilongiang 150030, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605