上海交通大学学报(自然版) ›› 2018, Vol. 52 ›› Issue (9): 1112-1119.doi: 10.16183/j.cnki.jsjtu.2018.09.016
逯程1,徐廷学1,王虹2
通讯作者:
徐廷学,男,教授,博士生导师,电话(Tel.):0535-6635483;E-mail:kvcelu@163.com.
作者简介:
逯程(1990-),男,山东省泰安市人,博士生,主要从事装备综合保障理论与技术研究.
基金资助:
LU Cheng,XU Tingxue,WANG Hong
摘要: 为了提高末制导雷达故障诊断的效率和精度,提出一种基于属性粒化聚类与回声状态网络(ESN)的故障诊断方法.定义一种基于属性值影响度的属性区分能力测量指标,并以此作为相似性度量依据,利用近邻传播(AP)聚类算法得到区分能力相当的若干属性粒,通过选取聚类中心属性来完成故障征兆属性的约简;在储备池构建中,采用Bienenstock-Cooper-Munro (BCM)规则进行连接权矩阵的预训练,在目标函数中添加L1/2范数惩罚项,以提高ESN储备池对样本的动态适应性;同时,采用半阈值迭代法对模型进行求解,通过末制导雷达信号处理模块的故障诊断实例,验证了所提方法的有效性和优越性.结果表明:与BP神经网络和传统ESN模型相比,所提方法的稳定性及诊断准确率更高;将其与基于属性重要度和AP聚类的属性粒化约简算法相结合,能够进一步提高故障诊断的效率和精度,其仿真实验的训练时间仅为 8.98s,诊断正确率可达 95.2%.
中图分类号:
逯程1,徐廷学1,王虹2. 基于属性粒化聚类与回声状态网络的末制导雷达故障诊断[J]. 上海交通大学学报(自然版), 2018, 52(9): 1112-1119.
LU Cheng,XU Tingxue,WANG Hong. Fault Diagnosis of Terminal Guidance Radar Based on Attribute Granulation Clustering and Echo State Network[J]. Journal of Shanghai Jiaotong University, 2018, 52(9): 1112-1119.
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摘要 27
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