阶跃激励与ReLU激活函数持续存在的充分条件
Sufficient Conditions for Persistency of Excitation with Step and ReLU Activation Functions
摘要：
本文定义了几何准则，然后使用这些准则来建立激励持续性的充分条件，该激励持续性具有由具有阶跃或ReLU激活函数的单隐层神经网络构造的向量函数。我们表明，这些条件下，采用参考系统跟踪时，通常在适应控制。我们证明了这种类型的线性参数化激活系统的数值结果，并表明参数估计收敛到真值的充分条件满足。
英文摘要：
This paper defines geometric criteria which are then used to establish sufficient conditions for persistency of excitation with vector functions constructed from single hidden-layer neural networks with step or ReLU activation functions. We show that these conditions hold when employing reference system tracking, as is commonly done in adaptive control. We demonstrate the results numerically on a system with linearly parameterized activations of this type and show that the parameter estimates converge to the true values with the sufficient conditions met.
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。
