Earticle

초록

영어

The coal and gas outburst is one of complex geological disasters and its prediction is influenced by a multiple of factors, such as coal gas, ground stress, physical and mechanical properties, and complex non-linear system, which cause the low prediction accuracy. It is a favorable scheme to use the nonlinear BP neural network for the prediction algorithm design. But, the traditional BP neural network algorithm has some defects, such as the slow convergence speed and falling into the local minimum value easily. In order to remedy the defects and improve the prediction accuracy of the coal and gas outburst effectively, the improved BP neural network prediction algorithm of the coal and gas outburst is put forward in this paper. The additional momentum is adopted to adjust the network weight and to speed up the network convergence speed, and then the speed of network learning is adjusted self-adaptively and the number of iterations is reduced. Finally, the simulation of prediction of the coal and gas outburst in mine is carried out. Compared with the traditional BP neural network, the improved algorithm shows its superiorities and provides the basis for the accurate prediction of coal mine disasters.

목차

Abstract
 1. Introduction
 2. Prediction Theory
 3. BP Prediction Algorithm
  3.1. Traditional BP Algorithm
  3.2. Defects and Improvement of Traditional BP Algorithm
  3.3. The Improved BP Prediction Algorithm
 4. The Experiment and Simulation
  4.1. The Experimental Data
  4.2. Experiment and Results of Prediction Performance
  4.3. Experiment and Results of Convergence Speed
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Li Cheng [ College of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, 161006, P. R. China ]
• Liu Yan-ju [ Computer Center, Qiqihar University, Qiqihar, Heilongjiang, 161006, P. R. China ]
• Zhang Hong-lie [ College of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, 161006, P. R. China ]

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