Earticle

초록

영어

This paper applies the rough set theory and artificial intelligence technology into the field of forestry diseases and pests. It proposed the model of forestry diseases and pests forecasting in the demonstration. Through the model validation and accuracy analysis, the accuracy of the result is high, and forecast results with the actual situation are match.. In addition, it proposed an improvement attribute reduction algorithm based on discernibility matrix. Reduced conditional attribute set of forestry diseases and pests forecasting based on the algorithm. Through the extraction and reduction of generation rules, obtained a new f forestry diseases and pests forecasting model based on rough set theory. By verifying, the model has achieved a good result.

목차

Abstract
 1. Introduction
 2. Rough Set Theory and the Application in Pests and Diseases Forecast
  2.1. Information Indicates
  2.2. Knowledge Reduction
 3. Forest Pests Forecasting Model
  3.1. Predict the Occurrence of Forest Pests
  3.2. Forest Pest Forecast
 4. Pests’ Prediction Based on Rough Set Theory
 5. Expert System Designs Based on Rough Set Theory
  5.1. System Architecture Design
  5.2. Design of Knowledge
  5.3. Design of Inference Engine
 6. System Implementation
  6.1. Forest Pests Expert System Implementation
  6.2. Analysis of Prediction Accuracy
 7. Conclusion
 Acknowledgements
 References

키워드

저자

• HongHan Zhang [ School of computer and information engineering Harbin University of Commerce Harbin, China ]
• YanRong Zhang [ School of computer and information engineering Harbin University of Commerce Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605