【摘要】：為研究面對戰(zhàn)場視圖如何捕捉到指揮員認(rèn)知經(jīng)驗的問題,深度學(xué)習(xí)中CNN可提供有力支持。但CNN的訓(xùn)練需要足夠的樣本數(shù)據(jù),目前難以獲得。針對戰(zhàn)爭中常見的戰(zhàn)場包圍態(tài)勢認(rèn)知及樣本不足問題進行了剖析,提出一種基于CNN的樣本不足包圍態(tài)勢認(rèn)知新方法,該方法利用CNN的非線性擬合功能及包圍態(tài)勢圖像的對稱特性,可在一定程度上獲得指揮員對包圍態(tài)勢的認(rèn)知經(jīng)驗。仿真實驗結(jié)果證明了方法的有效性和魯棒性。
[Abstract]:In order to study how to capture the commander's cognitive experience in the battlefield view, CNN can provide support in depth learning. But the CNN training needs enough sample data, is difficult to obtain at present. In this paper, the common problems of battlefield encirclement situation cognition and lack of samples are analyzed, and a new method based on CNN is proposed. In this method, the nonlinear fitting function of CNN and the symmetry characteristics of the encircling situation image can be used to obtain the commander's cognitive experience on the encircling situation to a certain extent. Simulation results show that the method is effective and robust.
【作者單位】： 中國人民解放軍國防大學(xué)信息作戰(zhàn)與指揮訓(xùn)練教研部;中國人民解放軍93682部隊;中國人民解放軍91053部隊;空軍工程大學(xué)防空反導(dǎo)學(xué)院;
【基金】：國家自然科學(xué)基金(61374179);國家自然科學(xué)基金青年科學(xué)基金(61703412) 軍民共用重大研究計劃聯(lián)合基金(U1435218) 中國博士后科學(xué)基金(2016M602996)
【分類號】：E11;TP18
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本文編號：2379766