【摘要】：為降低航空發(fā)動(dòng)機(jī)維修成本,增強(qiáng)維修等級(jí)決策的客觀性,提出一種基于奇異值分解的極限學(xué)習(xí)機(jī)(SVD-ELM)算法,推導(dǎo)基于奇異值分解(SVD)的極限學(xué)習(xí)機(jī)(ELM)輸出權(quán)重計(jì)算公式,從而有效地避免普通ELM在求解輸出權(quán)重時(shí)因矩陣奇異而導(dǎo)致無法求逆的問題。將SVD-ELM應(yīng)用于決策建模過程,提高決策模型的穩(wěn)定性。研究結(jié)果表明:相比于SVM,SVD-ELM和ELM的決策準(zhǔn)確率相同,且均比SVM的高,但SVD-ELM的模型穩(wěn)定性高于ELM,且SVD-ELM和ELM的測試耗時(shí)相差不大,說明這2種方法的計(jì)算量相當(dāng)。
[Abstract]:In order to reduce the maintenance cost of aero-engine and enhance the objectivity of maintenance grade decision, an algorithm of extreme learning machine (SVD-ELM) based on singular value decomposition (SVD) is proposed. The formula for calculating the output weight of (ELM) based on singular value decomposition (SVD) is derived. So it can avoid the problem that ordinary ELM can not solve the problem of matrix singularity in solving the output weight. SVD-ELM is applied to the decision-making modeling process to improve the stability of the decision model. The results show that compared with SVM,SVD-ELM and ELM, the accuracy of decision is the same and higher than that of SVM, but the model stability of SVD-ELM is higher than that of ELM, and the test time of SVD-ELM and ELM is not different, which indicates that the computation of the two methods is equal.
【作者單位】： 湖南大學(xué)信息科學(xué)與工程學(xué)院;湖南工學(xué)院數(shù)理科學(xué)與能源工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(71501068)~~
【分類號(hào)】：TP181;V263.6

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