Earticle

초록

영어

In the traditional model of fault diagnosis, neural network classification requires high demand for the number and completeness of samples with a problem cannot be overcome- -"the curse of dimensionality". While the actual bearing failure is a typical case of small sample with few samples and the number of different types of samples is asymmetrical and even not complete. And the pattern classification effects of the support rector machine in case of small sample are better. Therefore, according to the above comparative analysis, combined with the character of small samples of actual bearing failure mode, this paper selects to build classification model based on the support vector machines, and after researching, the model proved to be feasible.

목차

Abstract
 1. A Non-Linear Support Vector Machine
  1.1. Constructions of Nonlinear SVM
  1.2. Kernel Function Selection of SVM
  1.3. Classification SVM
 2. Typical Optimization Algorithm Analysis
  2.1. Simulated Annealing Algorithm
  2.2. Genetic Algorithms
  2.3. Ant Colony Algorithm
 3. Seeker Optimization Algorithm
  3.1. Basic Behavior
  3.2. Calculations on Search Step Length and Search Direction
  3.3. Simulated Annealing Algorithm
 4. Analysis on the Performance of SOA
  4.1. Simulation Analysis of Sphere Function
  4.2. Simulation Analysis of Schaffer Function
  4.3. Simulation Analysis of Rastrigin Function
 5. Parameters Optimization Process of Multi-Class SVM Based On SOA
 6. Conclusions
 References

키워드

저자

• Huimin Ge [ School of Automotive and Traffic Engineering, Jiangsu University ]
• Long Chen [ School of Automotive and Traffic Engineering, Jiangsu University ]
• Jun Liang [ School of Automotive and Traffic Engineering, Jiangsu University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605