Transactions of Nonferrous Metals Society of China The Chinese Journal of Nonferrous Metals

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中国有色金属学报

ZHONGGUO YOUSEJINSHU XUEBAO

第21卷    第4期    总第145期    2011年4月

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文章编号:1004-0609(2011)04-0894-07
基于模糊自适应变权重算法的采场冒顶函数链神经网络预报
左红艳,罗周全,王益伟,王爽英

(中南大学 资源与安全工程学院,长沙 410083)

摘 要: 为提高采场声发射事件率预报精度,将采场声发射事件率不同的单个预测模型的预测值作为函数链神经网络的原始输入值,并将原始输入值按正交的三角函数扩展得到的数值作为函数链神经网络扩展输入值,在分析函数链神经网络拟合充要条件的基础上,结合模糊自适应变权重算法计算函数链神经网络权重,对采场声发射事件率进行基于模糊自适应变权重算法的函数链神经网络预测,对其预测结果再进行函数链神经网络算法拟合,然后结合采场冒顶尖点突变模型的判别式对采场冒顶进行预报。某铅锌矿采场冒顶预报结果表明,基于模糊自适应变权重算法的函数链神经网络预测方法的预测误差小于0.3%,可实现采场冒顶精确预报。

 

关键字: 函数链神经网络;模糊自适应变权重算法;预测;采场冒顶;声发射

Prediction of functional link neural network of roof caving based on
 fuzzy adaptive variable weight method
ZUO Hong-yan, LUO Zhou-quan, WANG Yi-wei, WANG Shuang-ying

School of Resource and Safety Engineering, Central South University, Changsha 410083, China

Abstract:In order to enhance the predict precision about happening rate of acoustic emission in mine, the happening rate of acoustic emission in mine was forecasted based on functional link neural network due to fuzzy adaptive variable weight algorithm by using of making some forecasting values from different single forecasting model of happening rate of acoustic emission in mine as original input values of functional link neural network, making the original input values as patulous input values of functional link neural network after the original input values being extended according to the orthogonal trigonometric function, analyzing the necessary and sufficient conditions of functional link neural network fitting and calculating the weight of functional link neural network based on fuzzy adaptive variable weight algorithm. And the roof caving can be predicted when the forecasting results is fitted by functional link neural network algorithm and the discriminant of roof caving abrupt change model. The forecasting results of happening rate of acoustic emission in some lead and zinc mine reveal that the functional link neural network forecasting method based on fuzzy adaptive variable weight algorithm is higher than that of other forecasting model and its forecasting error is smaller than 0.3%. And the precision predicting roof caving is able to be realized due to the functional link neural network forecasting.

 

Key words: functional link neural network; fuzzy adaptive variable weight method; prediction; roof caving; acoustic emission

ISSN 1004-0609
CN 43-1238/TG
CODEN: ZYJXFK

ISSN 1003-6326
CN 43-1239/TG
CODEN: TNMCEW

主管:中国科学技术协会 主办:中国有色金属学会 承办:中南大学
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