一种基于数据复用的雷达对抗知识库设计方法
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摘要
针对目前历史训练数据量大、非结构化特点突出、实际利用率低的问题,提出一种基于历史数据挖掘的雷达对抗知识库设计方法。从实际应用出发,对知识库设计业务需求进行了分析;以此为基础,采用多库结构形式,引入大数据思想以及信息交互思想,提出了雷达对抗知识库构建框架,对子模块进行了具体设计,并从信息挖掘、多维快速索引以及决策链路预测三个层面,设计了知识库的关键算法。提出的方法为历史数据的有效利用提供了一种新的思路和新的方法,可为组训人员进行训练设计以及电子对抗战法研究提供一定支撑。
To solve the problems that the historic data are giant and has unstructured character and the low availability are obvious, a design method for radar countermeasures knowledge base based on historic data is proposed. Considering the real application, the requirement of knowledge base design is firstly analyzed. On this basis, using the multi-base structure, the radar countermeasures knowledge base structural frame is presented with the big data and information interaction measures. The sub-modules are detailed designed, and the key algorithms of knowledge base are detailed based on the information digging, the multi-dimensional quick indexing and the decision predicting layers. The method proposed provides a novel idea and measure for the effective uses of the historic data, which lays the foundation for the trainers making training design and electronic countermeasures research.
To solve the problems that the historic data are giant and has unstructured character and the low availability are obvious, a design method for radar countermeasures knowledge base based on historic data is proposed. Considering the real application, the requirement of knowledge base design is firstly analyzed. On this basis, using the multi-base structure, the radar countermeasures knowledge base structural frame is presented with the big data and information interaction measures. The sub-modules are detailed designed, and the key algorithms of knowledge base are detailed based on the information digging, the multi-dimensional quick indexing and the decision predicting layers. The method proposed provides a novel idea and measure for the effective uses of the historic data, which lays the foundation for the trainers making training design and electronic countermeasures research.
引文
[1] 陈修国.面向问题分析与决策专家系统的知识库系统研究[D].武汉:华中科技大学,2009.
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[3] 吕琳媛.复杂网络链路预测[J].电子科技大学学报,2010,39(5):651-661.
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[7] 彭宇,王建民,彭喜元.基于回声状态网络的时间序列预测方法研究[J].电子学报,2010,38(2A):148-154.
[8] Simone S,Dianhui W,Massimo P.A decentralized training algorithm for Echo State Networks in distributed big data applications [J].Neural Networks,2016,78:65-74.
[9] 王彤,张红梅.一种动态Huffman优化算法的设计与应用[J].空军工程大学学报(自然科学报),2005,6(2):76-78.
[10] Guangye Z,Chengren L,Tingting L.Outer synchronization investigation between WS and NW small-word networks with difference node numbers [J].Physica A:Statistical Mechanics and Its Applications,2016,457:506-513.
[2] 张吴波,史旅华,李贵荣.全文检索引擎Lucene系统模型与应用研究[J].软件导刊,2015,14(6):127-129.
[3] 吕琳媛.复杂网络链路预测[J].电子科技大学学报,2010,39(5):651-661.
[4] Liu X,Yu Q,Liao J.Fastdfs:a high performance distributed file system [J].ICIC express letters,Part B Applications:an international journal of research and surveys,2014,5(6):1741-1746.
[5] Milad S,Justin Z,Saied T.Using query logs to establish vocabularies in distributed information retrieval [J].Information Processing and Management,2007,43(1):169-180.
[6] Lü L,Zhou T.Link prediction in complex networks:a survey [J].Physica A:statistical Mechanics and its Applications,2011,390(6):1150-1170.
[7] 彭宇,王建民,彭喜元.基于回声状态网络的时间序列预测方法研究[J].电子学报,2010,38(2A):148-154.
[8] Simone S,Dianhui W,Massimo P.A decentralized training algorithm for Echo State Networks in distributed big data applications [J].Neural Networks,2016,78:65-74.
[9] 王彤,张红梅.一种动态Huffman优化算法的设计与应用[J].空军工程大学学报(自然科学报),2005,6(2):76-78.
[10] Guangye Z,Chengren L,Tingting L.Outer synchronization investigation between WS and NW small-word networks with difference node numbers [J].Physica A:Statistical Mechanics and Its Applications,2016,457:506-513.