• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Fast Learning of Grammar Production Probabilities in Radar Electronic Support
【24h】

Fast Learning of Grammar Production Probabilities in Radar Electronic Support

机译：雷达电子支持中语法生成概率的快速学习

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Although stochastic context-free grammars (SCFG) appear promising for the recognition and threat assessment of complex radar emitters in radar electronic support (ES) systems, the computational requirements for learning their production rule probabilities can be onerous. The two most popular methods, the inside-outside (IO) algorithm and the Viterbi score (VS) algorithm, are both iterative. IO maximizes the likelihood of a training data set, whereas VS maximizes the likelihood of its best parse trees. Even though VS is known to have lower overall computational costs in practice, both algorithms can be impractical for complex grammatical models. Several techniques have been previously developed to accelerate learning. In this paper, two fast variants of the traditional IO algorithm, known as graphical expectation-maximization (gEM(IO)) and tree-scanning (TS(IO)), are reviewed, along with a third technique called HOLA. In addition, two novel algorithms are proposed that apply the gEM (gEM(VS)) and TS (TS(VS)) principles to the Viterbi technique. An experimental protocol is defined and implemented so that the performance of all five techniques (gEM(IO), TS(IO), gEM(VS), TS(VS), and HOLA) can be compared using simulated training sets of complex radar signals. These techniques are compared from several perspectives—perplexity (the likelihood of a test data set), error rate on estimated states, time and memory complexity per iteration, and convergence time. Estimation of the average case and worst case execution time and storage requirements allow for the assessment of complexity, while computer simulations, performed using radar data sets, allow for the assessment of the other performance measures. The impact on performance of the number of sequences in the training set is observed. Results indicate that gEM(IO) and TS(IO) provide the same level of accuracy, yet the resources requirements depend on the ambiguity of the grammars. As expected, the gEM(VS) and TS(VS) -n-ntechniques provide significantly lower convergence times and time complexities in practice than gEM(IO) and TS(IO), for a comparable level of accuracy. All of these algorithms may provide a greater level of accuracy than HOLA, yet their computational complexities may be orders of magnitude higher.
机译：尽管随机上下文无关文法（SCFG）在识别和威胁评估雷达电子支持（ES）系统中的复杂雷达发射器方面似乎很有前途，但是学习其生产规则概率的计算要求却很繁重。内外（IO）算法和维特比评分（VS）算法是两种最受欢迎​​的方法，都是迭代的。 IO最大化训练数据集的可能性，而VS最大化其最佳解析树的可能性。即使在实践中已知VS具有较低的总体计算成本，但对于复杂的语法模型而言，这两种算法都是不切实际的。先前已经开发了几种技术来加速学习。在本文中，对传统IO算法的两个快速变体，即图形期望最大化（gEM（IO））和树扫描（TS（IO）），以及第三种称为HOLA的技术进行了回顾。另外，提出了两种新颖的算法，将gEM（gEM（VS））和TS（TS（TS））原理应用于Viterbi技术。定义并实施了一个实验协议，以便可以使用复杂雷达信号的模拟训练集比较所有五种技术（gEM（IO），TS（IO），gEM（VS），TS（VS）和HOLA）的性能。从多个角度比较了这些技术-复杂性（测试数据集的可能性），估计状态的错误率，每次迭代的时间和内存复杂性以及收敛时间。对平均情况和最坏情况的执行时间以及存储需求的估计可以评估复杂性，而使用雷达数据集执行的计算机模拟则可以对其他性能指标进行评估。观察到训练集中的序列数量对性能的影响。结果表明gEM（IO）和TS（IO）提供相同的准确性，但是资源要求取决于语法的歧义性。不出所料，与gEM（IO）和TS（IO）相比，gEM（VS）和TS（VS）-n技术在实践中提供了明显更低的收敛时间和时间复杂度，并且具有相当的准确性。所有这些算法都可以提供比HOLA更高的准确性，但是它们的计算复杂度可能要高几个数量级。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号