改进的神经网络算法在瓦斯涌出量预测中的应用  

Application of estimation based on improved BP algorithm

作　　者：母丽华[1] 张太发[1] 李焱[1] 

机构地区：[1]黑龙江科技学院理学院,哈尔滨150027

出　　处：《黑龙江科技学院学报》2011年第3期240-243,共4页Journal of Heilongjiang Institute of Science and Technology

基　　金：黑龙江省教育厅科学技术研究项目(11541323)

摘　　要：针对传统神经网络存在的收敛速度慢、容易陷入局部最小等缺陷,采用改良的BP算法———双权值迭代优化法,提高神经网络传统BP算法的训练速度。以三层神经网络为例,对权值进行优化。实验对比表明:双权值迭代优化法应用于瓦斯涌出量的预测,比一般BP网络有更高的预测精度和程序运行速度。This paper is aimed at an alternative to conventional neural network which suffers from a lower convergence rate and higher tendency to fall into partially smallest,by developing the improved BP algorithm,namely double weight iteration optimization method,to enhance the training speed of the neural conventional BP algorithm network.The method consists of taking three layer neural networks as the example,and,through the analysis and the optimization for weight value,applying the algorithm into predicting the mine gas emission.The experimental comparison shows that the double weight iteration optimization method gives a greater forecast accuracy and faster program operation than conventional BP algorithm network in predicting the mine gas emission.

关 键 词：BP神经网络 矿井瓦斯 涌出量 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TD712[自动化与计算机技术—控制科学与工程]