本文選題：ANFIS + 瓦斯?jié)B透率�。� 參考：《煤礦開采》2017年01期

【摘要】：為有效預測煤體瓦斯?jié)B透率,預警井下作業(yè)時瓦斯?jié)舛茸儎?利用神經(jīng)網(wǎng)絡的自適應學習能力和模糊推理系統(tǒng)的經(jīng)驗知識建立自適應神經(jīng)模糊推理系統(tǒng)(ANFIS)預測模型,并基于實驗室數(shù)據(jù)將其預測結(jié)果與BP神經(jīng)網(wǎng)絡模型和支持向量機(SVM)模型的預測值作對比。研究結(jié)果表明:ANFIS模型的收斂速度快,預測值與實測值相符度高;在誤差精度、訓練速度和收斂性等方面,其性能優(yōu)于其他兩種模型,可通過有效應力、瓦斯壓力、溫度和抗壓強度對瓦斯?jié)B透率進行高精度的預測。
[Abstract]:In order to effectively predict the gas permeability of coal body and predict the change of gas concentration in underground operation, an adaptive neural fuzzy inference system (ANFIS) prediction model is established by using the adaptive learning ability of neural network and the empirical knowledge of fuzzy inference system. The prediction results are compared with those of BP neural network model and support vector machine (SVM) model based on laboratory data. The research results show that the convergence speed of the 1: ANFIS model is fast, the predicted value is in good agreement with the measured value, and its performance is superior to that of the other two models in terms of error accuracy, training speed and convergence, which can be achieved by effective stress and gas pressure. Temperature and compressive strength are used to predict gas permeability with high accuracy.
【作者單位】： 鄭州大學管理工程學院;
【基金】：國家自然科學基金資助項目(71271194) 河南省高等學校重點科研項目計劃(16A630035) 河南省基礎與前沿技術(shù)研究計劃項目(162300410073) 教育部人文社會科學研究青年基金資助項目(11YJC630291)
【分類號】：TD712

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1 叢杉;崔志英;張渭源;;Prediction of Anthropometric Dimensions Based on Grey Incidence Analysis and ANFIS[J];Journal of Donghua University(English Edition);2007年03期

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本文編號：1840613