Earticle

초록

영어

Incorporating prior knowledge (PK) into learning methods is an effective means to improve learning performance. On the bases of requirements of engineering practice and the characteristics of knowledge representation of extension neural network (ENN), with the purpose of further improving the performance of ENN in engineering practice, a prior-knowledge-based ENN (PKENN) recognition method is proposed and applied in the application of safety status pattern recognition of coal mines in this paper. The PKENN recognition method effectively combines domain knowledge with training data set. The prior knowledge can provide additional information about the classical domain of characteristic vector that may compensate for the low quality of training data in a complex application environment. This method can set the initial weights of ENN, guide the learning of ENN and alleviate the learning burden. To demonstrate the validity and effectiveness of the proposed method, a real-world application on the geological safety status pattern recognition of coal mines is tested. Comparative experiments with existing methods and other ANN-based methods are conducted. The experimental results show that the proposed PKENN recognition method has a better performance.

목차

Abstract
 1. Introduction
 2. Theoretical Background
  2.1. Outline of Extension Theory
  2.2. Extension Neural Network
 3. The Mechanisms of PKENN-based Pattern Recognition Method
  3.1. Basic Design Idea of PKENN
  3.2. Knowledge Representation of ENN
  3.3. Advantages of PKENN-based Recognition Method
  3.4. Structure Design of ENN
  3.5. PKENN-based Recognition Algorithm
 4. Experimental Results and Discussion
  4.1. Background of the Application
 4.2. Comparison with Existing Methods
 4.3. Comparison with Other ANN-based Methods
 4.4. Tests of Error-containing Data
 5. Conclusions
 ACKNOWLEDGEMENTS
 References

키워드

저자

• Yu Zhou [ Key Laboratory of Innovation Method and Decision Management System of Guangdong Province, Guangzhou Guangdong, 510641, China, School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450011, China ]
• Lian Tan [ School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450011, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605