本文關(guān)鍵詞： 危險源識別 深度學習 極限學習機(ELM) 分類　出處：《計算機科學》2017年05期 　論文類型：期刊論文

【摘要】：危險源識別是民用航空管理的重要環(huán)節(jié)之一,危險源識別結(jié)果必須高度準確才能確保飛行的安全。為此,提出了一種基于深度極限學習機的危險源識別算法HIELM(Hazard Identification Algorithm Based on Extreme Learning Machine),設計了一種由多個深層棧式極限學習機(S-ELM)和一個單隱藏層極限學習機(ELM)構(gòu)成的深層網(wǎng)絡結(jié)構(gòu)。算法中,多個深層S-ELM使用平行結(jié)構(gòu),各自可以擁有不同的隱藏結(jié)點個數(shù),按照危險源領(lǐng)域分類接受危險源狀態(tài)信息完成預學習,并結(jié)合識別特征改進網(wǎng)絡輸入權(quán)重的產(chǎn)生方式。在單隱藏層ELM中,深層ELM的預學習結(jié)果作為其輸入,改進了反向傳播算法,提高了網(wǎng)絡識別的精確度。同時,分別訓練各深層S-ELM,緩解了高維數(shù)據(jù)訓練的內(nèi)存壓力和節(jié)點過多產(chǎn)生的過擬合現(xiàn)象。
[Abstract]:Hazard source identification is one of the important links in civil aviation management. The result of hazard source identification must be highly accurate in order to ensure the safety of flight. In this paper, an algorithm for identifying hazard sources based on depth limit learning machine (HIELM(Hazard Identification Algorithm Based on Extreme Learning machine) is proposed. A deep network structure is designed, which is composed of multiple deep stack extreme learning machines (S-ELM) and a single hidden layer extreme learning machine (ELM). Multiple deep S-ELM use parallel structure, each can have different number of hidden nodes, according to the classification of dangerous source domain to receive risk source state information to complete the pre-learning, In single hidden layer ELM, the pre-learning result of deep ELM is used as its input, and the back propagation algorithm is improved, and the accuracy of network recognition is improved. The S-ELMs are trained separately to alleviate the memory pressure and the over-fitting caused by the excessive number of nodes in the high-dimensional data training.
【作者單位】： 南京航空航天大學計算機科學與技術(shù)學院;
【基金】：江蘇省產(chǎn)學研聯(lián)合創(chuàng)新資金項目(SBY201320423)資助
【分類號】：TP18;V328
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本文編號：1550583