本文關(guān)鍵詞： Alpha穩(wěn)定分布(SαS) 高斯分布 概率神經(jīng)網(wǎng)絡(luò) 煤與瓦斯突出 預(yù)測　出處：《傳感技術(shù)學(xué)報(bào)》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(yù)測是煤礦安全生產(chǎn)的必要前提和保證。為了實(shí)現(xiàn)對煤與瓦斯突出危險(xiǎn)性快速、準(zhǔn)確和動(dòng)態(tài)預(yù)測,考慮煤與瓦斯突出多種影響因素。提出一種改進(jìn)的概率神經(jīng)網(wǎng)絡(luò)(PNN)煤與瓦斯突出預(yù)測模型。首先,引進(jìn)一種對稱Alpha穩(wěn)定分布(SαS),SαS有更廣泛的數(shù)學(xué)表達(dá),其徑向?qū)ΨQ特性可充當(dāng)PNN樣本層中的高斯分布。在SαS的基礎(chǔ)上,建立煤與瓦斯突出危險(xiǎn)性預(yù)測的SαS-PNN模型。將SαS-PNN模型應(yīng)用于國內(nèi)26個(gè)典型礦井的煤與瓦斯突出危險(xiǎn)性等級預(yù)測。預(yù)測結(jié)果表明:在3種不同的訓(xùn)練和測試下SαS-PNN模型仍具有良好的預(yù)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(xiǎn)性預(yù)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(xué)國土資源工程學(xué)院;淮陰工學(xué)院建筑工程學(xué)院;中國鋁業(yè)遵義氧化鋁有限公司;
【基金】：國家自然科學(xué)基金項(xiàng)目(51264018,51064012)
【分類號】：TD713;TP183

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本文編號：1527360