本文關鍵詞：基于模糊神經(jīng)網(wǎng)絡和證據(jù)理論的瓦斯突出評判策略　出處：《上海理工大學學報》2016年02期 　論文類型：期刊論文

  更多相關文章： 瓦斯突出 危險等級評判 模糊神經(jīng)網(wǎng)絡 證據(jù)理論

【摘要】：針對影響煤礦瓦斯突出因素的不確定性和復雜的非線性關系,不能夠利用經(jīng)典的數(shù)學理論建立精確的預測模型,將模糊神經(jīng)網(wǎng)絡和D-S證據(jù)理論有機結合,提出了基于模糊神經(jīng)網(wǎng)絡和D-S證據(jù)理論的煤礦瓦斯突出危險等級評判策略.首先對傳感器采集的待評判采掘面參數(shù)進行預處理,使用模糊神經(jīng)網(wǎng)絡得出第一步的融合結果,并將其進行歸一化處理,歸一化函數(shù)作為基本概率賦值函數(shù),然后將歸一化之后的數(shù)值作為基本概率分配值,再用D-S證據(jù)理論進行第二次數(shù)據(jù)融合,作出最終評判.實驗結果表明,該方法具有良好的適應性并能得到準確性較高的評判結果.
[Abstract]:In view of the uncertainty and complex nonlinear relationship of the factors affecting coal mine gas outburst, it is impossible to establish an accurate prediction model by using classical mathematical theory, and combine fuzzy neural network with D-S evidence theory organically. Based on fuzzy neural network and D-S evidence theory, this paper puts forward the evaluation strategy of coal mine gas outburst risk grade. Firstly, the parameters of mining face to be evaluated by sensors are pretreated. The fusion result of the first step is obtained by using the fuzzy neural network, and it is normalized. The normalized function is regarded as the basic probability assignment function, and then the normalized value is taken as the basic probability assignment value. Then the D-S evidence theory is used for the second data fusion and the final judgment is made. The experimental results show that the method has a good adaptability and can get a higher accuracy.
【作者單位】： 河南理工大學電氣工程與自動化學院;河南工業(yè)和信息化職業(yè)學院;
【基金】：河南省科技攻關計劃資助項目(102102210203)
【分類號】：TD713;TP183
【正文快照】： 煤炭作為我國工業(yè)生產(chǎn)的主要能源,當前在國家能源生產(chǎn)和一次消費結構中占據(jù)到大約70%,到2050年煤炭的使用率仍然會占到50%左右.因此,在今后很長的一段時間內(nèi),煤炭仍然是我國能源工業(yè)生產(chǎn)中的主要力量.但是,在煤炭的開采過程中,我國每年的煤礦瓦斯突出和爆炸事故時有發(fā)生,不僅

【參考文獻】

相關期刊論文 前7條

1 王友楠;方祖華;孫，

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