Earticle

초록

영어

Based on researching parameter optimization method of neural network deeply, a new parameter optimization method is presented and applied to surface defect online inspection system of cold rolled strips. The method takes advantages of small-samples fully, and can get a group of neural network parameters which can mostly express the neural network under a certain specific condition. The method is advantageous for its simplicity, easy to maintain and fast, it can be applied to many fields too, such as iron-steel industry, medicine. Experiments showed that a best recognition effect by using the parameters for neural network which are achieved by the new parameter optimization method can be got among all the parameters optimized randomly for surface defect of cold rolled strips.

목차

Abstract
 1. Introduction
 2. Theory for parameter optimization
  2.1 Acquirement of small-samples
  2.2 Parameter segmentation and encoding method
  2.3 Training and visual method in getting result
 3. Instance for processing parameter optimization
 4. Experiment results
 5. Conclusion
 6. References

저자

• Guifang Wu [ Electronic Information Engineering College, Henan University of Science & Technology, Luoyang 471003, China ]
• Yumin Ren [ Dept. of Children’s Clinic, Luoyang Dongfang Hospital, Luoyang 471003, China ]
• Yan Li [ Electronic Information Engineering College, Henan University of Science & Technology, Luoyang 471003, China ]
• Hoonsung Kwak [ Dept. of Computer Engineering, Engineering College, Chonbuk National University of Korea, Jeonju 561756, Korea ]
• Seyoung Jang [ Dept. of Computer Engineering, Engineering College, Chonbuk National University of Korea, Jeonju 561756, Korea ]

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