Earticle

초록

영어

In order to improve fault diagnosis precision and decrease misinformation diagnosis, rough set theory(RST) and RBF neural network (RBFNN) are introduced to overcome respective deficiency in order to propose a novel intelligence fault diagnosis(NCIRRFD) model and method in this paper. In this NCIRRFD method, the RST as a new mathematical tool is used to process inexact and uncertain knowledge in order to reduce decision tables for obtaining the minimum fault characteristic subset. At the same time, RST is used to serve for pretreatment data so that RBFNN structure is simplified and learning efficiency is improved in order to get an optimized NCIRRFD model for solving the inference complexity. An actual application case is selected to test and verify the proposed NCIRRFD method. The applied results show that the proposed NCIRRFD method can effectively eliminate false and improve the diagnostic accuracy.

목차

Abstract
 1. Introduction
 2. Rough Set Theory and RBF Neural Network
  2.1. Rough Set Theory
  2.2. RBF Neural Network
 3. An Intelligent Fault Diagnosis Method
 4. The Experiment Simulation and Application
 5. Conclusion
 References

키워드

저자

• Jing-Hua Zhu [ Department of Computer Science, Wenzhou Vocational & Technical College, Wenzhou 325035, PR China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605