系统工程与电子技术
• 系统工程 • 上一篇 下一篇
韩小孩, 张耀辉, 王少华, 徐隆洋
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HAN Xiao-hai, ZHANG Yao-hui, WANG Shao-hua, XU Long-yang
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摘要:
针对传统装备技术状态等级划分方法中存在的不足,提出了基于关联维数分析的装备技术状态等级划分方法。在此基础上,建立了基于径向基神经网络的模式识别模型,对重新划分技术状态等级后的样本数据进行训练,实现了装备技术状态等级模式识别。案例分析表明,该方法可有效改进原有技术状态等级划分不准确的问题,提高技术状态评估的准确性。
Abstract:
In order to solve problems in the grade classification of equipment’ condition with the traditional ways, the method based on the analysis of correlation dimension is proposed. And on that basis, we built the radial basis function (RBF) neural network and trained it by sample data which we got previously. Then the grade of verification samples is identified by the net. As shown in the case, the proposed method can effectively mitigate the problem in the equipment’s condition evaluation.
韩小孩, 张耀辉, 王少华, 徐隆洋. 基于关联维数分析的装备技术状态评估[J]. 系统工程与电子技术, doi: 10.3969/j.issn.1001-506X.2016.01.18.
HAN Xiao-hai, ZHANG Yao-hui, WANG Shao-hua, XU Long-yang. Equipment’s condition evaluation based on the analysis of correlation dimension[J]. Systems Engineering and Electronics, doi: 10.3969/j.issn.1001-506X.2016.01.18.
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链接本文: https://www.sys-ele.com/CN/10.3969/j.issn.1001-506X.2016.01.18
https://www.sys-ele.com/CN/Y2016/V38/I1/110