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概率积分法预计参数选取的神经网络模型
引用本文:郭文兵,邓喀中,邹友峰.概率积分法预计参数选取的神经网络模型[J].中国矿业大学学报,2004,33(3):322-326.
作者姓名:郭文兵  邓喀中  邹友峰
作者单位:1. 焦作工学院,资源与材料工程系,河南,焦作,454000;中国矿业大学,环境与测绘学院,江苏,徐州,221008
2. 中国矿业大学,环境与测绘学院,江苏,徐州,221008
3. 焦作工学院,资源与材料工程系,河南,焦作,454000
基金项目:国家自然科学基金项目(50174051),河南省自然科学基金项目(0311053100),河南省优秀中青年骨干教师基金项目
摘    要:在综合分析概率积分法参数与地质采矿条件之间关系的基础上,采用人工神经网络方法建立了概率积分法参数选取的模型.模型采用改进的BP优化算法,运用我国典型的地表移动观测站资料作为学习训练样本和测试样本。对网络模型的计算结果与实测值进行了对比分析.分析结果表明:用人工神经网络方法求算概率积分法参数结果更接近于实际.对提高开采沉陷预计精度具有积极意义.

关 键 词:概率积分法  地质采矿  神经网络模型  地表移动参数  开采沉陷
文章编号:1000-1964(2004)03-0322-05
修稿时间:2003年5月29日

Artificial Neural Network Model for Predicting Parameters of Probability-Integral Method
GUO Wen-bing.Artificial Neural Network Model for Predicting Parameters of Probability-Integral Method[J].Journal of China University of Mining & Technology,2004,33(3):322-326.
Authors:GUO Wen-bing
Affiliation:GUO Wen-bing~
Abstract:On the basis of analyaing the relationship between parameters of probability-integral method and geologic and mining conditions the model to calculate the parameters of probability-integral method was established by applying the theory of artificial neural network (ANN). A large amount from data of observation stations was used as learning and training samples to train and test the artificial neural network model by optimal arithmetic. The calculation results of the ANN model and the observed values were analyzed and compared with each other. The results show that it is more precise to predicting the parameters of probability-integral method by ANN technology and can make a contribution to enhance the precision of the predicted prarmeters for mining subsidence.
Keywords:probability-integral method  artificial neural networks  parameters of surface moveme-nt  mining subsidence
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