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English(EN) A Unified Particle Filter LSTM for Data-Driven Process Simulation

新型统一粒子滤波器LSTM增强过程模拟

研究人员开发了一种新颖的统一粒子滤波器LSTM(Unified PF-LSTM),用于数据驱动的过程模拟。该模型通过维护和更新加权的循环状态假设集,解决了标准循环神经网络的局限性,使其能够从不完整的事件日志中更好地推断潜在的过程条件。在三个真实的急诊科数据集上对Unified PF-LSTM进行了评估,与现有基线相比,在复制路由、持续时间和系统级行为方面表现出优越的性能。 AI

影响 这种新模型可以提高具有复杂、部分观察到的动态(如医疗保健)领域的模拟准确性。

排序理由 该集群包含一篇详细介绍数据驱动过程模拟新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新型统一粒子滤波器LSTM增强过程模拟

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该集群包含一篇详细介绍数据驱动过程模拟新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Parvin Malekzadeh, Opher Baron, Dmitry Krass ·

    一种统一的粒子滤波器LSTM用于数据驱动的过程模拟

    arXiv:2609.01967v1 Announce Type: new Abstract: Data-driven process simulation aims to generate realistic case trajectories from historical event logs without requiring an explicitly specified model of the underlying dynamics. Deep sequence models can capture complex temporal dep…