周期性激励系统的分层事件触发学习及其在无线传感器网络中的应用
Hierarchical Event-triggered Learning for Cyclically Excited Systems with Application to Wireless Sensor Networks
AI总结：
本文提出一种针对周期性激励非线性系统的分层事件触发学习方法，通过自动识别周期模式并分层更新模型来减少通信负载，在无线神经假体足部俯仰角测量中实现70%通信减少且误差小于1°。
摘要：
通信负载是许多实时系统中的限制因素。事件触发状态估计和事件触发学习方法通过仅当基于先前传输的数据无法充分预测信息时才发送信息来减少网络通信。本文针对具有周期性激励的非线性离散时间系统提出了一种事件触发学习方法。该方法自动识别数据中的周期性模式——即使这些模式反复变化——并且每当当前数据能够根据先前周期准确预测时，就减少通信负载。尽管如此，原始信号与接收信号之间的有界误差得到保证。用于预测的周期性激励模型以分层方式更新，即，仅当更新少量模型参数不足时才执行完整模型更新。非参数统计检验确保仅在周期性激励以高概率发生变化时才进行模型更新。所提出方法的有效性通过无线实时测量反馈控制神经假体中人类足部俯仰角的应用实例得到证明。实验结果表明，通信负载可减少70%，而测量角度与接收角度之间的均方根误差小于1°。
英文摘要：
Communication load is a limiting factor in many real-time systems. Event-triggered state estimation and event-triggered learning methods reduce network communication by sending information only when it cannot be adequately predicted based on previously transmitted data. This paper proposes an event-triggered learning approach for nonlinear discrete-time systems with cyclic excitation. The method automatically recognizes cyclic patterns in data - even when they change repeatedly - and reduces communication load whenever the current data can be accurately predicted from previous cycles. Nonetheless, a bounded error between original and received signal is guaranteed. The cyclic excitation model, which is used for predictions, is updated hierarchically, i.e., a full model update is only performed if updating a small number of model parameters is not sufficient. A nonparametric statistical test enforces that model updates happen only if the cyclic excitation changed with high probability. The effectiveness of the proposed methods is demonstrated using the application example of wireless real-time pitch angle measurements of a human foot in a feedback-controlled neuroprosthesis. The experimental results show that communication load can be reduced by 70 % while the root-mean-square error between measured and received angle is less than 1°.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
