通过伪谱配点实现时滞系统的稀疏识别
Sparse identification of time delay systems via pseudospectral collocation
AI总结：
针对离散时滞非线性系统的稀疏识别问题，提出基于伪谱配点的ODE逼近方法，仅估计最大时滞以降低计算量并提升性能。
摘要：
我们提出了一种实用方法，用于对具有离散时滞的系统进行非线性动力学稀疏识别。该方法通过伪谱配点，用常微分方程组逼近底层时滞模型。为最小化重构误差，新策略避免对所有可能的多个未知时滞进行优化，而仅识别最大时滞。因此，计算负担大幅降低，并提升了近期直接处理时滞系统的实现方法的性能。
英文摘要：
We present a pragmatic approach to the sparse identification of nonlinear dynamics for systems with discrete delays. It relies on approximating the underlying delay model with a system of ordinary differential equations via pseudospectral collocation. To minimize the reconstruction error, the new strategy avoids optimizing all possible multiple unknown delays, identifying only the maximum one. The computational burden is thus greatly reduced, improving the performance of recent implementations that work directly on the delay system.
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。
