• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Information Theory >Diffusion-Based Adaptive Distributed Detection: Steady-State Performance in the Slow Adaptation Regime
【24h】

Diffusion-Based Adaptive Distributed Detection: Steady-State Performance in the Slow Adaptation Regime

机译：基于扩散的自适应分布式检测：慢适应状态下的稳态性能。

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

This paper examines the close interplay between cooperation and adaptation for distributed detection schemes over fully decentralized networks. The combined attributes of cooperation and adaptation are necessary to enable networks of detectors to continually learn from streaming data and to continually track drifts in the state of nature when deciding in favor of one hypothesis or another. The results in this paper establish a fundamental scaling law for the steady-state probabilities of miss detection and false alarm in the slow adaptation regime, when the agents interact with each other according to distributed strategies that employ small constant step-sizes. The latter are critical to enable continuous adaptation and learning. This paper establishes three key results. First, it is shown that the output of the collaborative process at each agent has a steady-state distribution. Second, it is shown that this distribution is asymptotically Gaussian in the slow adaptation regime of small step-sizes. Third, by carrying out a detailed large deviations analysis, closed-form expressions are derived for the decaying rates of the false-alarm and miss-detection probabilities. Interesting insights are gained from these expressions. In particular, it is verified that as the step-size $mu $ decreases, the error probabilities are driven to zero exponentially fast as functions of $1/mu $ , and that the exponents governing the decay increase linearly in the number of agents. It is also verified that the scaling laws governing the errors of detection and the errors of estimation over the network behave very differently, with the former having exponential decay proportional to $1/mu $ , while the latter scales linearly with decay proportional to $mu $ . Moreover, and interestingly, it is shown that the cooperative strategy allows each agent to reach the same detection performance, in terms of detection error exponents, of a centralized stochastic-gradient solution. The results of this paper are illustrated by applying them to canonical distributed detection problems.
机译：本文研究了完全分散网络上分布式检测方案的协作与适应之间的紧密相互作用。合作和适应的组合属性对于使检测器网络能够从流数据中不断学习，并在决定支持一种假设或另一种假设时连续跟踪自然状态下的漂移是必不可少的。本文的结果建立了一个基本的比例定律，用于当慢速适应机制中的智能体根据采用小的恒定步长的分布式策略进行交互时，漏检和误报警的稳态概率的稳态定标。后者对于持续适应和学习至关重要。本文建立了三个关键的结果。首先，它表明在每个代理程序上协作过程的输出具有稳态分布。其次，表明在小步长的慢速适应状态下，该分布是渐近高斯分布的。第三，通过进行详细的大偏差分析，得出错误警报和漏检概率的衰减率的闭式表达式。从这些表达中获得有趣的见解。特别地，已经证实，随着步长$ mu $的减小，错误概率随$ 1 / mu $的函数呈指数快地趋于零，并且控制衰减的指数随主体数量线性增加。还证实了控制检测误差和估计误差的缩放定律在网络上的行为有很大不同，前者的指数衰减与$ 1 / mu $成正比，而后者的线性衰减与$ mu $成正比。 。此外，有趣的是，该策略显示了协作策略允许每个代理在集中式随机梯度解决方案的检测错误指数方面达到相同的检测性能。通过将其应用于规范的分布式检测问题来说明本文的结果。

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