论文标题
在合作非持久激发条件下的随机动态系统的分布式稀疏识别
Distributed Sparse Identification for Stochastic Dynamic Systems under Cooperative Non-Persistent Excitation Condition
论文作者
论文摘要
本文考虑了无线传感器网络上的分布式稀疏识别问题，以便通过使用邻居的局部信息来协同估计随机动态系统的未知稀疏参数向量。提出了分布式稀少平方算法，是通过最小化作为累积局部估计误差和L_1验证项的线性组合的局部信息标准来提出的。提出了估计误差的上限和所提出算法的自适应预测因子的遗憾。此外，通过基于局部观察数据设计合适的自适应加权系数，在合作的非经验性激发条件下获得了零元素的集合收敛。结果表明，拟议的分布式算法可以以合作的方式运行良好，即使单个传感器都无法完成估计任务。我们的理论结果是在不依赖于现有文献中常用的回归信号的独立性假设而获得的。因此，我们的结果预计将应用于随机反馈系统。最后，提供了数值模拟以证明我们理论结果的有效性。
This paper considers the distributed sparse identification problem over wireless sensor networks such that all sensors cooperatively estimate the unknown sparse parameter vector of stochastic dynamic systems by using the local information from neighbors. A distributed sparse least squares algorithm is proposed by minimizing a local information criterion formulated as a linear combination of accumulative local estimation error and L_1-regularization term. The upper bounds of the estimation error and the regret of the adaptive predictor of the proposed algorithm are presented. Furthermore, by designing a suitable adaptive weighting coefficient based on the local observation data, the set convergence of zero elements with a finite number of observations is obtained under a cooperative non-persistent excitation condition. It is shown that the proposed distributed algorithm can work well in a cooperative way even though none of the individual sensors can fulfill the estimation task. Our theoretical results are obtained without relying on the independency assumptions of regression signals that have been commonly used in the existing literature. Thus, our results are expected to be applied to stochastic feedback systems. Finally, the numerical simulations are provided to demonstrate the effectiveness of our theoretical results.
