本文關(guān)鍵詞： 危險(xiǎn)源識(shí)別 深度學(xué)習(xí) 極限學(xué)習(xí)機(jī)(ELM) 分類　出處：《計(jì)算機(jī)科學(xué)》2017年05期 　論文類型：期刊論文

【摘要】：危險(xiǎn)源識(shí)別是民用航空管理的重要環(huán)節(jié)之一,危險(xiǎn)源識(shí)別結(jié)果必須高度準(zhǔn)確才能確保飛行的安全。為此,提出了一種基于深度極限學(xué)習(xí)機(jī)的危險(xiǎn)源識(shí)別算法HIELM(Hazard Identification Algorithm Based on Extreme Learning Machine),設(shè)計(jì)了一種由多個(gè)深層棧式極限學(xué)習(xí)機(jī)(S-ELM)和一個(gè)單隱藏層極限學(xué)習(xí)機(jī)(ELM)構(gòu)成的深層網(wǎng)絡(luò)結(jié)構(gòu)。算法中,多個(gè)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(diǎn)個(gè)數(shù),按照危險(xiǎn)源領(lǐng)域分類接受危險(xiǎn)源狀態(tài)信息完成預(yù)學(xué)習(xí),并結(jié)合識(shí)別特征改進(jìn)網(wǎng)絡(luò)輸入權(quán)重的產(chǎn)生方式。在單隱藏層ELM中,深層ELM的預(yù)學(xué)習(xí)結(jié)果作為其輸入,改進(jìn)了反向傳播算法,提高了網(wǎng)絡(luò)識(shí)別的精確度。同時(shí),分別訓(xùn)練各深層S-ELM,緩解了高維數(shù)據(jù)訓(xùn)練的內(nèi)存壓力和節(jié)點(diǎn)過(guò)多產(chǎn)生的過(guò)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(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：江蘇省產(chǎn)學(xué)研聯(lián)合創(chuàng)新資金項(xiàng)目(SBY201320423)資助
【分類號(hào)】：TP18;V328
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本文編號(hào)：1550583