English(EN) ZNO: Stable Rational Neural Operators in the Z-Domain for Discrete-Time Dynamic

ZNO推出用于离散时间动力学的稳定有理神经网络算子

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-04 08:59

研究人员推出了Z域神经网络算子（ZNO），这是一种新颖的因果神经网络算子，专为离散时间动态系统设计。ZNO的架构利用z平面中参数化的稳定低秩有理滤波器，弥补了算子学习方法中通常侧重于连续时间问题的空白。该模型在涉及单位圆附近极点和长记忆动力学的稳定有理系统识别任务中表现出特别的有效性。 AI

影响为离散时间系统识别引入了专门的神经网络算子，有可能提高特定动态系统的准确性。

排序理由该集群包含一篇arXiv预印本，详细介绍了离散时间动态系统的新模型架构。

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arXiv cs.LG TIER_1 English(EN) · Xianli Zhu, Jia Yin · 2026-05-05 04:00

ZNO：离散时间动态的Z域稳定有理神经网络算子

arXiv:2605.02356v1 Announce Type: new Abstract: We introduce the Z-Domain Neural Operator (ZNO), a causal neural operator whose layers are stable low-rank multiple-input multiple-output (MIMO) rational filters parameterized directly in the $z$-plane. ZNO addresses a limitation of…
arXiv cs.LG TIER_1 English(EN) · Jia Yin · 2026-05-04 08:59

ZNO：离散时间动态的Z域稳定有理神经网络算子

We introduce the Z-Domain Neural Operator (ZNO), a causal neural operator whose layers are stable low-rank multiple-input multiple-output (MIMO) rational filters parameterized directly in the $z$-plane. ZNO addresses a limitation of existing operator learning methods, many of whi…