Earticle

초록

영어

Precision of pests, in stored grain insect population density, has been a hot and difficult research in pest detection and control system. The accuracy of prediction of pest density will directly affect to warehouse grain temperature and the food quality etc. In order to improve the accuracy, the paper which using the depth study method, established an insects density prediction mode with the depth of the belief network as the core. The model is applied to the algorithm of deep learning predictive control. According to the temperature and humidity of the grain obtained from the actual measurement and the initial density of the pest, we predicted the pest density. Simulation results show that the root mean square error is small between the predictive value and actual value, high prediction accuracy. The deep learning algorithm is applied to the population density of pests is effective.

목차

Abstract
 1. Introduction
 2. The Basic Theory of Deep Learning Algorithm
 3. The Research Insect Population Density Prediction Model
  3.1. The Prediction Identification Model Design of Insect Population Density
  3.2. The Prediction Identification Model Training Algorithm Based on the Pest  Population Density
 4. The Prediction and Result of Population Density for Grain Stored Insects
  4.1. The Collection of Training Samples
  4.2. The Pretreatment of the Data
  4.3. The Experimental Results and Analysis
 5. Conclusion
 References

키워드

저자

• Wu Jian-Jun [ School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China ]
• Dang Hao [ School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China ]
• Li Miao [ School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China ]
• Sun Fu-Yan [ School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China ]
• Zhu Yu-Hua [ School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China ]
• Zhen Tong [ School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China / Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing, China ]
• Zou Bing-Qiang [ Shandong College of Information Technology, Software Department Weifang, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605