發(fā)布時間：2018-02-23 19:35

  本文關鍵詞： Alpha穩(wěn)定分布(SαS) 高斯分布 概率神經(jīng)網(wǎng)絡 煤與瓦斯突出 預測　出處：《傳感技術學報》2017年07期 　論文類型：期刊論文

【摘要】：較高精度的煤與瓦斯突出預測是煤礦安全生產(chǎn)的必要前提和保證。為了實現(xiàn)對煤與瓦斯突出危險性快速、準確和動態(tài)預測,考慮煤與瓦斯突出多種影響因素。提出一種改進的概率神經(jīng)網(wǎng)絡(PNN)煤與瓦斯突出預測模型。首先,引進一種對稱Alpha穩(wěn)定分布(SαS),SαS有更廣泛的數(shù)學表達,其徑向?qū)ΨQ特性可充當PNN樣本層中的高斯分布。在SαS的基礎上,建立煤與瓦斯突出危險性預測的SαS-PNN模型。將SαS-PNN模型應用于國內(nèi)26個典型礦井的煤與瓦斯突出危險性等級預測。預測結果表明:在3種不同的訓練和測試下SαS-PNN模型仍具有良好的預測效果,其誤判率分別為7.69%、11.54%和15.38%。說明該模型可為煤礦開采中煤與瓦斯突出危險性預測提供了一種可能的思路。
[Abstract]:High precision prediction of coal and gas outburst is the necessary premise and guarantee of coal mine safety production. In order to realize fast, accurate and dynamic prediction of coal and gas outburst, Considering the influence factors of coal and gas outburst, an improved probabilistic neural network (PNN) model for predicting coal and gas outburst is proposed. The radial symmetry can act as Gao Si distribution in PNN sample layer. The S 偽 S-PNN model of coal and gas outburst risk prediction was established. The S 偽 S-PNN model was applied to the prediction of coal and gas outburst risk grade in 26 typical mines in China. The prediction results showed that S 偽 S-PNN model was used in 3 different training and testing conditions. Still have good prediction results, The misjudgment rates are 7.69% and 15.38%, respectively. It shows that the model can provide a possible way to predict the risk of coal and gas outburst in coal mining.
【作者單位】： 昆明理工大學國土資源工程學院;淮陰工學院建筑工程學院;中國鋁業(yè)遵義氧化鋁有限公司;
【基金】：國家自然科學基金項目(51264018,51064012)
【分類號】：TD713;TP183

【參考文獻】

相關期刊論文 前10條

1 王佳信;周宗紅;趙婷;余洋先;龍剛;李春陽;;基于Alpha穩(wěn)定分布概率神經(jīng)網(wǎng)絡的圍巖穩(wěn)定性分類研究[J];巖土力學;2016年S2期

2 付華;司南楠;魯俊杰;王雨虹;徐耀松;;基于bi-LWCA-ENN煤與瓦斯突出危險性預測[J];傳感技術學報;2016年08期

3 謝國民;謝鴻;付華;閆孝Y，

本文編號：1527360