本文選題：煤與瓦斯突出 + 信號(hào)挖掘��； 參考：《工礦自動(dòng)化》2015年06期

【摘要】：針對(duì)傳統(tǒng)關(guān)聯(lián)聚類算法因難以捕捉異常信號(hào)非線性隨機(jī)變化而造成采煤作業(yè)中特征信號(hào)檢測(cè)不準(zhǔn)確的問題,提出一種基于特征關(guān)聯(lián)挖掘算法的煤與瓦斯突出信號(hào)挖掘方法。該方法利用小波變換提取煤礦井下作業(yè)區(qū)狀態(tài)信號(hào)特征,為煤與瓦斯突出信號(hào)挖掘提供依據(jù);計(jì)算煤礦井下作業(yè)區(qū)狀態(tài)信號(hào)特征之間的關(guān)聯(lián)度,實(shí)現(xiàn)煤與瓦斯突出特征信號(hào)挖掘。實(shí)驗(yàn)結(jié)果表明,該方法可提高煤與瓦斯突出信號(hào)挖掘的準(zhǔn)確性。
[Abstract]:In order to solve the problem of inaccurate detection of characteristic signals in coal mining operation because of the difficulty in capturing the nonlinear random variation of abnormal signals in traditional association clustering algorithm, a method of mining coal and gas outburst signal based on characteristic association mining algorithm is proposed. The coal and gas outburst signal mining provides the basis, calculates the correlation degree between the characteristics of the state signal in the coal mine operation area, and realizes the coal and gas outburst characteristic signal mining. The experimental results show that this method can improve the accuracy of coal and gas outburst signal mining.
【作者單位】： 平頂山學(xué)院計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：河南省重點(diǎn)科技攻關(guān)項(xiàng)目(142102210225)
【分類號(hào)】：TD713.2

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本文編號(hào)：2043292