本文選題：鐵路突發(fā)事件 + 距離熵��； 參考：《鐵道學(xué)報(bào)》2017年08期

【摘要】：將基于聚類分析與改進(jìn)距離熵權(quán)SVD分解的方法應(yīng)用到鐵路突發(fā)事件應(yīng)急決策中,為具有高維、大量數(shù)據(jù)特點(diǎn)的鐵路突發(fā)事件應(yīng)急救援提供一種實(shí)用、快速、智能的輔助決策方法。在對案例特征屬性梳理的基礎(chǔ)上,提出改進(jìn)距離熵的權(quán)重獲取方法。在給出合理選取聚類中心點(diǎn)個數(shù)方法的前提下,結(jié)合聚類方法和加權(quán)SVD分解構(gòu)建鐵路突發(fā)事件應(yīng)急輔助決策模型。通過具體算例說明該方法用于鐵路突發(fā)事件輔助決策的過程。案例分析表明,基于聚類分析與改進(jìn)距離熵權(quán)SVD分解的方法能夠較準(zhǔn)確且快速地滿足鐵路應(yīng)急決策需求,為鐵路突發(fā)事件應(yīng)急輔助決策提供了新方法、新思路。
[Abstract]:The method based on clustering analysis and improved distance entropy weight SVD decomposition is applied to railway emergency decision making, which provides a practical and fast method for railway emergency rescue with high dimension and large amount of data. Intelligent auxiliary decision making method. On the basis of combing the characteristic attributes of cases, an improved method of weight acquisition based on distance entropy is proposed. On the premise of reasonably selecting the number of cluster center points, combined with clustering method and weighted SVD decomposition, a railway emergency assistant decision model is constructed. An example is given to illustrate the application of this method to railway emergency decision-making. The case study shows that the clustering analysis and the improved distance entropy weight SVD decomposition method can meet the railway emergency decision demand more accurately and quickly, and provide a new method and a new idea for the railway emergency decision.
【作者單位】： 西南交通大學(xué)交通運(yùn)輸與物流學(xué)院;蘭州交通大學(xué)自動化與電氣工程學(xué)院;
【基金】：國家自然科學(xué)基金(71173177) 甘肅省青年科技基金(145RJYA242) 蘭州交通大學(xué)青年科學(xué)基金(2015041)
【分類號】：U298.6

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1 滕麗娜;佟德純;陳兆能;;非線性時間序列的迭代SVD降噪法[A];振動工程學(xué)報(bào)（工程應(yīng)用專輯）[C];2001年

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本文編號：1952010