Quality evaluation of aerospace software using parallel neural networks

doi:10.11990/jheu.201812091

CIOMP OpenIR

	Quality evaluation of aerospace software using parallel neural networks
	Y. Song,X. Shen and H. Li
	2020
发表期刊	Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University
ISSN	10067043
卷号	41 期号:4 页码:595-600
摘要	Aiming at the quality evaluation of aerospace software systems that are strongly influenced by subjective experts and poor scalability resulting in low evaluation accuracy, a quality evaluation method based on parallel neural networks is proposed that effectively combines a neural network with DS evidence theory. Selected software quality evaluation indexes are input into independently parallel neural networks to obtain preliminary evaluation results. The DS evidence theory is then used to combine the preliminary evaluation results for the final evaluation. The proposed method is validated in the quality evaluation of an aerospace camera system. The accuracy of the proposed method reached 95.23%, the training time was 576.00 ms, and the evaluation processing time was 77.50 ms. With advantages of higher accuracy, less processing time for training and evaluation, the method satisfies the quality evaluation requirements of an aerospace software system. Copyright 2020 Journal of Harbin Engineering University.
DOI	10.11990/jheu.201812091
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收录类别	EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/64819
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Y. Song,X. Shen and H. Li. Quality evaluation of aerospace software using parallel neural networks[J]. Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University,2020,41(4):595-600.
APA	Y. Song,X. Shen and H. Li.(2020).Quality evaluation of aerospace software using parallel neural networks.Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University,41(4),595-600.
MLA	Y. Song,X. Shen and H. Li."Quality evaluation of aerospace software using parallel neural networks".Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University 41.4(2020):595-600.

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