An incorporate genetic algorithm based back propagation neural network model for coal and gas outburst intensity prediction
The traditional GABP model used in complex coal and gas outbursts prediction, which trains the back-propagation neural networks (BPNN) by Genetic Algorithm (GA), is provided with some limitations, such as massive time-consuming, optimal stop condition of GA pretreatment indeterminacy, independency and complex task of great importance. To overcome these problems, a new method of coal and gas outbursts intensity prediction by Incorporate Genetic Algorithm Based Back Propagation Neural Network (IGABP) is applied to determine parameters of BPNN automatically and propose an efficient GA which reduces its iterative computation time for enhancing the training capacity of BPNN. First, improved GA is based on single population model among continuous generation model and used the modified self-adapted crossover rate, crossover strategy, self-adapted stop criterion, as well as special survival condition. Second, BP operator is introduced into the evolution of GA operations, improving the standard GA optimization of random search and self-guiding optimization searching. To show the validity of the proposed method, we compare it with traditional GABP and IGABP using a dataset. The results show that the IGABP model can effectively overcome the inadequacies of the traditional model, its operating efficiency and forecast performance are improved significantly.
coal and gas outburst outburst intensity prediction incorporate genetic algorithm based back propagation neural network improved model BP operator
Yang Min Wang Yun-jia Cheng Yuan-ping
School of Environmental Science & Spatial Informatics, China University of Mining & Technology, Xuzh School of Safety Engineering, China University of Mining & Technology, Xuzhou, Jiangsu 221116, China
国际会议
The 6th International Conference on Mining Science & Technology ICMST 2009(第六届国际矿业科学技术大会)
徐州
英文
1-8
2009-10-18（万方平台首次上网日期，不代表论文的发表时间）